Developing A Contextual Combinational Approach for Predictive Analysis of Users Mobile Phone Trajectory Data in LBSNs
محورهای موضوعی : Data MiningFatemeh Ghanaati 1 , Gholamhossein Ekbatanifard 2 , Kamrad Khoshhal Roudposhti 3
1 - Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Department of Computer Engineering, Lahijan branch, Islamic Azad University, Lahijan, Iran
3 - Department of Computer Engineering, Lahijan branch, Islamic Azad University, Lahijan, Iran
کلید واژه: LBSN, Trajectory data, Contextual Information, GRU,
چکیده مقاله :
Today, smartphones, due to their ubiquity, have become indispensable in human daily life. Progress in the technology of mobile phones has recently resulted in the emergence of several popular services such as location-based social networks (LBSNs) and predicting the next Point of Interest (POI), which is an important task in these services. The gathered trajectory data in LBSNs include various contextual information such as geographical and temporal contextual information (GTCI) that play a crucial role in the next POI recommendations. Various methods, including collaborating filtering (CF) and recurrent neural networks, incorporated the contextual information of the user’ trajectory data to predict the next POIs. CF methods do not consider the effect of sequential data on modeling, while the next POI prediction problem is inherently a time sequence problem. Although recurrent models have been proposed for sequential data modeling, they have limitations such as similarly considering the effect of contextual information. Nonetheless, they have a separate impact as well. In the current study, a geographical temporal contextual information-extended attention gated recurrent unit (GTCI-EAGRU) architecture was proposed to separately consider the influence of geographical and temporal contextual information on the next POI recommendations. In this research, the GRU model was developed using three separate attention gates to consider the contextual information of the user trajectory data in the recurrent layer GTCI-EAGRU architecture, including timestamp, geographical, and temporal contextual attention gates. Inspired by the assumption of the matrix factorization method in CF approaches, a ranked list of POI recommendations was provided for each user. Moreover, a comprehensive evaluation was conducted by utilizing large-scale real-world datasets based on three LBSNs, including Gowalla, Brightkite, and Foursquare. The results revealed that the performance of GTCI-EAGRU was higher than that of competitive baseline methods in terms of Acc@10, on average, by 42.11% in three datasets.
Today, smartphones, due to their ubiquity, have become indispensable in human daily life. Progress in the technology of mobile phones has recently resulted in the emergence of several popular services such as location-based social networks (LBSNs) and predicting the next Point of Interest (POI), which is an important task in these services. The gathered trajectory data in LBSNs include various contextual information such as geographical and temporal contextual information (GTCI) that play a crucial role in the next POI recommendations. Various methods, including collaborating filtering (CF) and recurrent neural networks, incorporated the contextual information of the user’ trajectory data to predict the next POIs. CF methods do not consider the effect of sequential data on modeling, while the next POI prediction problem is inherently a time sequence problem. Although recurrent models have been proposed for sequential data modeling, they have limitations such as similarly considering the effect of contextual information. Nonetheless, they have a separate impact as well. In the current study, a geographical temporal contextual information-extended attention gated recurrent unit (GTCI-EAGRU) architecture was proposed to separately consider the influence of geographical and temporal contextual information on the next POI recommendations. In this research, the GRU model was developed using three separate attention gates to consider the contextual information of the user trajectory data in the recurrent layer GTCI-EAGRU architecture, including timestamp, geographical, and temporal contextual attention gates. Inspired by the assumption of the matrix factorization method in CF approaches, a ranked list of POI recommendations was provided for each user. Moreover, a comprehensive evaluation was conducted by utilizing large-scale real-world datasets based on three LBSNs, including Gowalla, Brightkite, and Foursquare. The results revealed that the performance of GTCI-EAGRU was higher than that of competitive baseline methods in terms of Acc@10, on average, by 42.11% in three datasets.
[1] L. Huang, Y. Ma, Sh. Wang, Y. Liu, “An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation”, Journal of IEEE Transactions on Services Computing, vol. 12, 2019, pp. 1-13.
[2] J. Manotumruksa, C. Macdonald, I. Ounis, “A Contextual Attention Recurrent Architecture for Context- Aware Venue Recommendation”, in 18th ACM SIGIR Conference on Research and Development in Information Retrieval, 2018, pp. 555-564.
[3] K. Kala, M. Nandhini,” Context Category Specific sequence aware Point of Interest Recommender System with Multi Gated Recurrent Unit”, Journal of Ambient Intelligence and Humanized Computing, 2019, https://doi.org/10.1007/s12652-019-01583-w.
[4] C. Liu, J. Liu, J. Wang, S. Xu, H. Han, Y. Chen, “An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation”, International Journal of Geo-Information, vol. 8, No. 8, 2019, pp.355-373.
[5] S. Wang, Z. Bao, J. Culpepper, G. Cong, “A Survey on Trajectory Data Management, Analytics, and Learning”, ACM Computing Surveys, vol. 54, No.3, 2020, pp. 1-33.
[6] J. Feng Y. Li, C. Zhang, F. Sun, F. Meng, A. Guo, D. Jin, “DeepMove: Predicting Human Mobility with Attentional Recurrent Networks”, in 18th ACM IW3C2 Conference on International World Wide Web, 2018, pp. 1459-1468.
[7] Q. Guo, Z .Sun, J. Zhang, Y. Theng, “An Attentional Recurrent Neural Network for Personalized Next Location Recommendation”, in 34th ACM AAAI Conference on Artificial Intelligence, 2020, pp. 83-90.
[8] D.Yao,C. Zhang, J. Huang, J. Bi, “SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories”, in 17th ACM CIKM Conference on Information and Knowledge Management, 2017 ,pp. 2411-2414.
[9] L. Zhang, Z. Sun, J. Zhang, H. Kloeden, F. Klanner, “Modeling hierarchical category transition for next POI recommendation with uncertain check-ins”, Journal of Information Sciences, Elsevier, vol.515, 2019, pp. 169-190.
[10] L. Chang, W. Chen, J. Huang, Ch. Bin, W. Wang, “Exploiting multi-attention network with contextual influence for point-of-interest recommendation”, Journal of Applied Intelligence, vol. 51, 2021, pp. 1904–1917.
[11] L. Huang, Y. Ma, Y. Liu, K. He, “DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation”, Journal of ACM Transactions on Internet Technology, Vol.21, No.2, 2020, pp. 1–27.
[12] G. Christoforidis, P. Kefalas, A. Papadopoulos, Y. Manolopoulos, “RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings”, Journal of Knowledge and Information Systems, Vol. 63, No.4, 2019, pp. 791-817.
[13] J. Manotumruksa, C. Macdonald, I. Ounis, “A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation”, in 17th ACM CIKM Conference on Information and Knowledge Management, 2017, pp. 1429-1438.
[14] D. Yang, D Zhang, V. Zheng, Z. Yu, “Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs”, Journal of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.45, No.1, 2014, pp. 129 – 142.
[15] M. Quadrana, P. Cremonesi, D. Jannach, “Sequence-Aware Recommender Systems”, Journal of ACM Computing Surveys, Vol.51, No.4, 201, pp. 1–36.
[16] Q. Cui, Y. Tang, S. Wu, L. Wang, “Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction”, in PAKDD Conference on Knowledge Discovery and Data Mining, 2019, pp. 289-301.
[17] Q. Gao, F. Zhou, G. Trajcevski, K. Zhang, T. Zhong, F. Zhang, “Predicting Human Mobility via Variational Attention”, in IW3C2 Conference on International World Wide Web Conference Committee, 2019, pp. 2750–2756.
[18] A.Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need”, in 31th NIPS Conference on Neural Information Processing System, 2017, pp. 5998-6008.
[19] Y. Chen, C. Long, G.Cong, C. Li, “Context-aware Deep Model for Joint Mobility and Time Prediction”,in 13th ACM WSDM Conference on Web Search and Data Mining, 2020, pp. 106-114.
[20] S. Rendle, C. Freudenthaler, Z. Gantner, L. Thieme, “BPR: Bayesian Personalized Ranking from Implicit Feedback”, in 25th ACM UAI Conference on Uncertainty in Artificial Intelligence, 2009, pp. 452–461.
[21] E.Cho, S. Myers, J. Leskovec, “Friendship and Mobility: User Movement in Location-Based Social Networks”, in 17th ACM KDD Conference on Knowledge Discovery and Data Mining, 2011, pp. 1082–1090.
[22] P. Zhao, H. Zhu, Y. Liu, J. Xu, F. Zhuang, V. Sheng, X. Zhou, “Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation”,. in 33th AAAI Conference on Artificial Intelligence, 2019, pp. 5877-5884.
[23] A M. Islam, M. M. Mohammad, S. S. Das, M. E. Ali, “A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations”, 2020, arXiv:2011.10187v1.
[24] C. Zheng, D. Tao, “Attention-Based Dynamic Preference Model for Next Point-of-Interest Recommendation”. in 15th Springer WASA Conference on Wireless Algorithms, Systems, and Applications, 2020, pp. 768–780.
[25] D. K. Bokde, Sh. Girase, D. Mukhopadhyay, “Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey”, International Journal of Advance Foundation and Research in Computer, vol.1, 2014, pp. 111-118.
[26] M. Gan, L. Gao, “Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks”, International Journal of Geo-Information (IJGI), Vol.8, No.6, 2019, pp. 279-294.
[27] X. Meng, J. Fang, “A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations”, Journal of IEEE Access, Vol.8, 2020, pp. 226309- 226323.
[28] Q. Yuan, G. Cong, Z. Ma, A. Sun, N. Thalmann, “Time-aware Point-of-interest Recommendation”, in 36th ACM SIGIR Conference on Research and development in Information Retrieval, 2013, pp. 363–372.
[29] P. Wang, H. Wang, H. Zhang, F. Lu, S. Wu, “A Hybrid Markov and LSTM Model for Indoor Location Prediction”, Journal of IEEE Access, Vol.7, 2019, pp. 185928 – 185940.
[30] J. Li, G. Liu, C. Yan, C. Jiang, “LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation”, IEEE Transactions on Computational Social Systems, Vol.6, No.3, 2019, pp. 430 – 440.
[31] L.Yao, Q. Z. Sheng, Y. Qin, X. Wang, A. Shemshadi, Q. He, “Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization”, in 38th.ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 1007–1010.
[32] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T. Chua, “Neural collaborative filtering”, in 26th ACM IW3C2Conference on World Wide Web Conference Committee, 2017, pp. 173-182.
[33] Q. Liu, S.Wu, L. Wang, T. Tan, “Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts”, in 30th ACM AAAI Conference, 2016 , pp. 194–200.
[34] S. Kumar, M.I. Nezhurina, “An ensemble classification approach for prediction of user’s next location based on Twitter data”, Journal of Ambient Intelligence and Humanized Computing, Vol.10, No. 11, 2018, pp. 4503-4513.
[35] Q. Liu, S. Wu, D. Wang, Z. Li, L. Wang, “Context-Aware Sequential Recommendation”, in ICDM Conference on Data Mining, IEEE, 2016, pp. 1053-1058.
[36] D. Bokde, S. Girase, D. Mukhopadhya, “Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey”, Procedia Computer Science, Vol.49, 2015, pp. 136-146.
[37] D. Lian, Y. Wu, Y. Ge, X. Xie, E. Chen, “Geography-Aware Sequential Location Recommendation”, in 26th ACM ICGKDD Conference on Knowledge Discovery and Data Mining, 2020, pp. 2009–2019.
[38] K. Yang, J. Zhu, “Next POI Recommendation via Graph Embedding Representation from H-Deepwalk on Hybrid Network”, Journal of IEEE Access, Vol 7, 2019, pp. 171105 – 171113.
[39] D. P. Kingma, J.L. Ba, “A Method for Stochastic Optimization”, in International Conference for Learning Representations, 2015, arXiv:1412.6980v.
http://jist.acecr.org ISSN 2322-1437 / EISSN:2345-2773 |
Journal of Information Systems and Telecommunication
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Developing A Contextual Combinational Approach for Predictive Analysis of Users Mobile Phone Trajectory Data in LBSNs |
Fatemeh Ghanaati1, Gholamhossein Ekbatanifard2*, Kamrad Khoshhal Roudposhti2
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1. Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran 2. Department of Computer Engineering, Lahijan branch, Islamic Azad University, Lahijan, Iran |
Received: 09 Dec 2021/ Revised: 12 Feb 2022/ Accepted: 22 March 2022 |
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Abstract
Today, smartphones, due to their ubiquity, have become indispensable in human daily life. Progress in the technology of mobile phones has recently resulted in the emergence of several popular services such as location-based social networks (LBSNs) and predicting the next Point of Interest (POI), which is an important task in these services. The gathered trajectory data in LBSNs include various contextual information such as geographical and temporal contextual information (GTCI) that play a crucial role in the next POI recommendations. Various methods, including collaborating filtering (CF) and recurrent neural networks, incorporated the contextual information of the user’ trajectory data to predict the next POIs. CF methods do not consider the effect of sequential data on modeling, while the next POI prediction problem is inherently a time sequence problem. Although recurrent models have been proposed for sequential data modeling, they have limitations such as similarly considering the effect of contextual information. Nonetheless, they have a separate impact as well. In the current study, a geographical temporal contextual information-extended attention gated recurrent unit (GTCI-EAGRU) architecture was proposed to separately consider the influence of geographical and temporal contextual information on the next POI recommendations. In this research, the GRU model was developed using three separate attention gates to consider the contextual information of the user trajectory data in the recurrent layer GTCI-EAGRU architecture, including timestamp, geographical, and temporal contextual attention gates. Inspired by the assumption of the matrix factorization method in CF approaches, a ranked list of POI recommendations was provided for each user. Moreover, a comprehensive evaluation was conducted by utilizing large-scale real-world datasets based on three LBSNs, including Gowalla, Brightkite, and Foursquare. The results revealed that the performance of GTCI-EAGRU was higher than that of competitive baseline methods in terms of Acc@10, on average, by 42.11% in three datasets.
Keywords: LBSN; Trajectory Data; Contextual Information; GRU.
1- Introduction
Nowadays, people widely use location-based social networks (LBSNs) and enjoy location-based services (LBSs) using their mobile devices for sharing their locations with others by making check-ins at locations or points of interests (POIs) that they have visited, including shops, museums, and restaurants [1]. The massive record of users’ check-in data provides a chance to conduct research on people’s mobility behaviors, in particular, for POI recommendation systems [2,3]. In addition, governments can use predictions about people’s future destinations and develop better transportation and scheduling strategies for alleviating traffic jams and handling crowd congestions [5,6,7,8]. Some geographical and temporal information exists in a user’s historical check-in sequence [4,9], having different effects on recommending the next POI. In this study, it was attempted to separately consider this contextual information to better train the proposed model. Human mobility is extremely complex and diverse; therefore, many previous studies were unable to simply determine the offering of the next POI recommendation [4,6]. Matrix factorization (MF) and other collaborative filtering (CF) techniques have widespread use for recommending a list of personally ranked POIs to the users [2]. Typically, approaches to MF include contextual information about the user. This helps provide valuable recommendations to users who lack enough historical check-ins and is generally referred to as the cold-start problem. However, the employment of collaboration filtering (CF)-based methods complicates the processing of sequence data and capturing of dynamic user’s preferences [2,6,11]. As a result, the ongoing challenges lie in the manner of integrating the information of different features to accurately model users’ complex behavioral preferences and then recommending reliable POIs [13].
Recurrent neural networks (RNNs) have recently been successfully applied to sequential recommender systems [1,4,8,15]. Thus, long-term dependencies can be captured by the hidden states of recurrent methods [4,16]. Many types of recurrent-based approaches have considered geographical and temporal factors to enhance the performance of POI recommendation algorithms [2,4,11,12,15]. Nonetheless, the present RNN-based POI recommendation methods face the alleviation of the cold-start problem [11]. In this regard, one of the excellent choices is to incorporate RNN-based POI recommendation methods with the MF method to enjoy the benefits of each one [2]. The user’s historical check-in behaviors do not significantly pose any problems in predicting the next behavior; hence, it is necessary to take only the important information into serious consideration [1,11]. Therefore, the attention mechanism (AM) has been proposed to deal with this challenge. The AM can enhance the capability of the neural network in capturing long-term dependencies and boost the ability to interpret neural networks [18]. In this study, the idea of the AM was used to address the most important contextual information.
1-1- Motivations
This study focused on the next POI recommendation through modeling check-in sequences and considering geographical and temporal contextual influences separately and proposed a novel geographical temporal contextual information extended attention gated recurrent unit (GTCI-EAGRU) for the next POI recommendation. Among the recurrent models, the GRU model is highly simple and does not include many parameters in contrast to the long-short term memory (LSTM) model. In addition, this model can ignore the earlier unit hidden state, which is impossible with the traditional RNN [4,6]. Thus, a GRU network was developed to model check-in sequences while paying attention to geographical distances and time intervals between two successive check-ins [19]. It is noteworthy that any piece of contextual information needs individual consideration during modeling since the effects of contextual information on user behavior are different [2,3]. Further, the GRU network was upgraded by inspiration from the AM to consider more important contextual information.
Furthermore, factorization approaches were employed, and the preference score was computed by the dot product. Following the prediction scores, it is possible to recommend top-k POIs to a user, and there is a high chance that the user will go there if the score is higher. The Bayesian personalized ranking (BPR) framework [20] learned the parameters of GTCI-EAGRU. In the last stage, three general datasets were utilized to conduct extensive experiments. Five up-to-date POI recommendation methods were compared with Brightkite, Gowalla, and Foursquare to evaluate the model.
1-2- Main Contributions
1- The proposed architecture is presented by combining the development of the GRU model with the MF method, which aims to apply the strengths of the models and reduce the challenges of each of these methods. According to the MF method, in the CF approach, places visited on social networks by a user on social networks can affect the next POI of other users on those networks. However, CF-based approaches are weak in modeling sequential data and do not consider the effect of sequential data on modeling, while the next POI prediction problem is inherently a time sequence problem. Although recurrent models have been proposed for sequential data modeling, they have limitations. The traditional RNN model cannot integrate the corresponding check-in contextual information into the modeling. Newer recurrent models also consider the effect of temporal and spatial contextual information similarly, while they have a separate effect.
Therefore, there is a need to develop these models. In the recurrent layer of the proposed architecture, a development of the GRU model is presented using three attention gates that consider the contextual information separately and in terms of their importance.
2- Within the recurrent layer of the proposed architecture, the flexibility of the GRU model is employed, and the GRU model was expanded following the attention-based approach. Moreover, three additional attention gates were proposed, including timestamp contextual attention gate (Gts), geographical contextual attention gate (Gge), and temporal contextual attention gate (Gte). The Gts controls the influence of timestamp earlier visited locations, whereas Gge and Gte control the effect of the hidden state of the earlier recurrent unit based on geographical distances and time intervals between two successive check-ins, respectively. This innovation makes it possible to extend the model to another context.
3- In this research, user contextual information is classified into two categories of absolute and transitional content information. The first category includes check-in timestamp and geographical coordinates and the second one consists of the time interval and geographical distance between two successive consecutive check-ins. Our proposed architecture considers two types of absolute and transitional contextual information separately. This category focuses on developing a model to consider more contextual information in the future.
4. Some comprehensive experiments were conducted on three large-scale real-world datasets, namely, Brightkite, Gowalla [21], and Foursquare [14] that are widely used in related studies to predict the user POI in LBSNs. The aim was to show the effectiveness of the proposed GTCI-EAGRU architecture for the next POI recommendation.
1-3- Problem Statement
Human mobility prediction is important for a wide spectrum of LBSN applications, and the next POI recommendation is one of the usages of predicting people’s mobility [1]. In some LSBNs, users share their location by registering check-ins. The check-ins gathered in LBSNs contain geographical and temporal contextual information (TCI), and each piece of information has a separate effect on predicting the user’s next location [3, 8]. In previous studies, some restrictions were applied for dividing sequence into different check-in trajectories such as using the time interval of less than six hours [1]. Nonetheless, applying restrictions for the time interval and geographical distance, when considering registered check-ins in data preprocessing, is not a proper approach for the mentioned purpose. The AM can address the mentioned issue. Instead of using multiple assumptions to consider the time interval or geographical distance constraints between two check-ins, it can be addressed by automatic weighting given to the model inputs inspired by the AM.
According to evidence [2], CF-based approaches have weaknesses in sequential data modeling and fail to consider the effect of sequential data, while the problem of the next location prediction is inherently a matter of time sequence (Challenge 1). Traditional recurrent models are unable to consider contextual information, but this information is highly important in determining the next POI (Challenge 2). Meanwhile, some earlier studies, based on recurrent models, consider the effect of temporal and geographical contextual information (GCI) to be the same, while they have a different effect (Challenge 3). Furthermore, according to [11], some proposed architectures, which are a combination of recurrent models and AM, are highly complex (Challenge 4). In this work, the GTCI-EAGRU model was proposed to address the above-mentioned challenges.
1-4- Organizations
The remaining parts of this research are as follows: The related methods are briefly reviewed in Section 2. Sections 3 and 4 describe some preliminaries to the study and the details of the GTCI-EAGRU network, respectively. In Section 5, an illustration of the experiments is presented, followed by providing the results of the proposed method. Finally, Section 6 summarizes conclusions and an outline for future works.
2- Related Works
This section classifies related studies under three approaches generally used for the next POI recommendations, including CF, RNN, and AM. Table 1 provides a summary of related works with their challenges considered in our research.
Table 1. summarize of related works
Model Name | Model Approach | Method summery | challenges |
---|---|---|---|
[28] Unified method | CF based | Believing that time plays an important role in POI recommendations and defining a new problem, namely, the time-aware POI recommendation to recommend POIs for a given user at a specified time in a day | Focusing on temporal contextual information and paying less attention to geographic contextual information |
[30] LORI | CF based | Applying a confidence coefficient for each user in the integration process and designing a learning-to-rank based algorithm to train confidence coefficients | Not taking into consideration time interval and geographical distance |
[33] ST-RNN | RNN based | Extending RNN and using a transition matrix for capturing the temporal cyclic effect and geographical influence | Vanishing gradient problem in long sequence due to the use of the traditional RNN |
[22] STGN | RNN based | Modifying the basic LSTM model slightly by introducing gates and cells to capture short- and long-term preferences | Considering the same effect for temporal and geographical contextual information |
[8] SERM | RNN based | Jointly learning the embedding of multiple factors (user, location, time, and keywords) and the transition parameters of an RNN in a unified framework | Not taking into account the geographical distance in the training of this model |
[35] CA-RNN | RNN based | Employing adaptive context-specific input matrices and adaptive context-specific transition matrices | Using a traditional RNN model and restrictions on paying attention to the contextual information, low performance |
[1] ATST-LSTM | AM and RNN based | Developing an attention-based spatiotemporal LSTM network to focus on the relevant historical check-in records in a check-in sequence selectively using the spatiotemporal contextual information | Encountering with high complexity of implementation and a lack of attention to the scarcity |
[6] Deep Move | AM and RNN based | Capturing complex dependencies and multi-level periodicity nature of humans using embedding, GRU, and AM | Not taking into account the time interval between two checks to model the behavioral pattern of user check-ins |
[11] DAN-SNR | AM based | Makes use of the self-AM. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively | Using only the attention mechanism and had low performance rather than applying recurrent neural networks for modeling the sequential influence and social influence |
3- Preliminaries
The research problem is formulated, and the applied preliminaries in this study are presented in the following section.
3-1- Notations and Definitions
Table 2 presents some primary notations used in this study.
Definition 1 (Check-in): A check-in is an action that a user takes under a geographical and temporal context. In addition, it is a registration of a location in the LBSN that contains geographical and temporal information. When a user u checks in a location l (including latitude and longitude) with venue-Id v at the timestamp t, the check-in record can be modeled as a quadruple: cu, v, t < u, l, v, t >.
Definition 2 (Check-in sequence): A user’s check-in sequence or Su is a set of all user check-ins.
Definition 3 (Trajectory): Given a user u, a trajectory t is a sequence of chronologically ordered check-in associated with u. For example tru: < u, l1, v1, t1 >,…,< u, li, vi, ti >,…,< u, lk, vk, tk >, where tru is the trajectory of a user u before time tk. Here, a trajectory set Tr (u) is used to denote all the trajectories of user u.
Definition 4 (POI): In LBSNs, a POI is a spatial item related to a geographical location and known as a venue, including a hotel or an office. In this research, POI is represented by v, and the set of POIs is demonstrated as V={v1, v2,...}. Each POI v has a unique identifier and geographical coordinate, consisting of geographical latitude and geographical longitude.
Definition 5 (the next POI recommendation): Given all users’ trajectories, the aim of the next POI recommendation is to predict the most likely location 𝑣𝑘 that a user 𝑢 will visit at a certain time point 𝑡𝑁+1.
Definition 6 (POI recommendations): Given a set of users’ check-in sequences Su and a set of POIs V, the POI recommendation task is to recommend top-k POIs that are preferable for user u.
Table 2: Notations and descriptions used in this study
Notations | Descriptions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
u, l, v, & t | User, location (including latitude and longitude), venue or POI, and timestamp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
cu, v, & t | A check-in recorded by user u in POI v and timestamp t | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
lat v & lng v | Latitude and longitude of POI v (i.e., geographical coordinates of POI v) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Δt & Δg | Time interval and geographical distance between two successive check-ins | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Su | A set of all check-ins generated by user u | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Us, V, & T | Sets of users, POIs, and timestamp | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
vτu | POI visited by user u at time step | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
gτu & tτu | Vector representations of geographical and temporal intervals | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
tru | A sequence of chronologically ordered check-ins related to u | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Tr (u) | All trajectories from user u | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ϕu | The latent factor of user u | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ϕv | The latent factor of POI v | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ϕt | The latent factor of timestamp t | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
d | The number of latent dimensions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
v+ | A set of positive POIs (visited venues) for each user uUs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
v- | A set of positive negative POIs (unvisited venues) for each user uUs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Sigmoid function |
Algorithm 1: Training of GTCI-EAGRU | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Input: Set of users 𝑈s and set of historical check-in sequences Su | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Output: GTCI-EAGRU model { 𝛩 } | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
//construct training instances | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1. | Initialize D=Usu Du = ∅ Du is a set of check-in trajectory samples combined with negative POIs of 𝑢 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2. | For each user 𝑢 𝑈s do | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
3. | For each check-in sequence Su = {st1u , st2u ,…, stnu } do | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
4. | Get the set of negative samples | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
5. | For each check-in activity in Su do | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
6. | Compute the embedded vector vτu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
7. | Compute the geographical contexts vector gτu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
8. | Compute the temporal contexts vector tτu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
9. | End for | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
10. | Add a training instance ({vτu , gτu , tτu },{}) into Du | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
11. | End for | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
12. | End for | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
//train the model | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
13. | Initialize the parameter set 𝛩 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
14. | While (exceed(maximum number of iterations)==FALSE) do | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
15. | For each user 𝑢 in 𝑈 do | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
16. | Randomly select a batch of instances 𝐷𝑏𝑢 from 𝐷𝑢 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
17. | Find 𝛩 minimizing the objective (23) with 𝐷𝑏𝑢 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
18. | End for | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
19. | End While | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
20. | Return the set of parameter 𝛩 |
Dataset | #Users | #Check-ins | #POIs | Density |
Brightkite | 915 | 676721 | 7527 | 0.0982 |
Gowalla | 1047 | 614340 | 5011 | 0.1170 |
Foursqure | 615 | 108195 | 19245 | 0.0091 |
A leave-one-out evaluation method was adopted to evaluate the efficiency of the proposed GTCI-EAGRU architecture based on earlier works [2], [3]. Each user’s most recent check-in was taken as the base, and 100 POIs, which had not been visited before, were randomly selected for this purpose. They were the testing set, and the other remaining check-ins were considered as the training set. The task of the GTCI-EAGRU was to rank those 100 venues for each user as their preferred contexts (i.e., timestamp, time interval, and geographical distance), aiming at ranking highest the recent, ground truth check-in. Following Manotumruksa et al. [2] and Kala et al. [3], the researchers set the dimension of the latent factors d and hidden layers hτ of the proposed GTCI-EAGRU architecture: d = 10. As mentioned before, Gaussian distribution [32] was employed for the random initialization of the recurrent layer’s parameters, and Adam Optimizer [39] was utilized for optimizing the parameters because it had a faster convergence compared to the stochastic gradient descent optimization, which automatically adjusts the learning rate for each iteration. In addition, the batch size and the dropout rate were set to 256 and 0.2, respectively, to prevent overfitting.
5-2- Comparison
The following five up-to-date methods were compared to validate the efficiency of the GTCI-EAGRU in the next POI recommendation task. Table 4 summarizes these methods into different aspects. Based on data, they are categorized into MF-, RNN-, and AM-based approaches. The compared models are also classified according to the use of GCI and TCI.
A brief description of these models is given below:
STGN: Spatio-temporal gated network was proposed by Zhao et al. [22], and improved the LSTM network, in which STGs are introduced for capturing the Spatio-temporal relationships between successive check-ins. By introducing new gates and cells to capture short- and long-term preferences, STGN modified the basic LSTM model.
ARNN: An attentional RNN was proposed by Guo et al. [7] to jointly model the transition regularities and sequential regularity of similar locations (neighbors). Using embedding, knowledge graph, LSTM, and AM, the ARNN captured sequential, spatial, temporal, and semantic influences.
GeoSAN: By introducing a new loss function, Lian et al. [37] resolved the sparsity issue. GeoSAN represents the hierarchical gridding of each GPS point with a self-attention based geography encoder for better use of geographical information.
DRCF: To benefit from the traditional RNN to model the sequential order of users’ check-ins, Manotumruksa et al. [13] extended NeuMF. DRCF has two components each having its recurrent layer.
CARA: By employing embedding, GRU, and two gating mechanisms, Manotumruksa et al. [2] captured various types of the impact of different contextual information.
Following earlier works [6-8,22], the current study used prediction accuracy (Acc@k, k = 10) for evaluating the performance of the above-mentioned methods and checking if the ground-truth location can be found in the top-k recommendation list. Generally, the Accuracy@ is defined by Eq. (27) as follows [29]:
(27)
Table 4. Summary of all the baseline methods used in this study
Methods | Approaches and Contextual Information | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MF | RNN | AM | GCI | TCI | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
STGN | × |
| × |
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ARNN | × |
|
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GeoSAN | × | × |
|
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| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DRCF |
|
| × | × | × | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CARA |
|
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| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GTCI-EAGRU |
|
|
|
|
|
Methods | Acc@10 | ||
Brightkite | Gowalla | Foursquare | |
STGN | 0.2020 | 0.5231 | 0.3017 |
ARNN | - | 0.2336 | 0.4285 |
GeoSAN | 0.6425 | 0.6028 | 0.4867 |
DRCF | 0.7363 | - | 0.8805 |
CARA | 0.7385 | - | 0.8851 |
GTCI-EAGRU | 0.9751 | 0.9606 | 0.8901 |
Note.: STGN: Spatio-temporal gated network; ARNN: Attentional Recurrent Neural Network; GeoSAN: Geography-aware sequential recommender based on the Self-Attention Network; DRCF: Deep Recurrent Collaborative Filtering; CARA: Contextual attention recurrent architecture; GTCI-EAGRU: Geographical temporal contextual information-extended attention gated recurrent unit.
The comparison of the experimental results of the models demonstrated that the use of AM alone (i.e., the GeoSAN model) has not increased prediction accuracy. Moreover, the experimental results of other previous studies (e.g., DAN-SNR) revealed lower evaluation metrics values. Although the STGN model separately considered the GCI and TCI, it did not use the attenuation mechanism approach. It applied the LSTM model and was less prediction accurate compared to models that employed the GRU such as CARA. Although the ARNN model applied the LSTM model, it had a higher accuracy prediction in comparison with the STGN model due to the use of the attenuation mechanism. Similar to the STGN, it had less prediction accuracy compared to models that considered the GRU model.
The GeoSAN model only uses the AM for location recommendation, and despite considering geographical and TCI, it is less prediction accurate than DRCF and CARA models. The DRCF model pays attention to the sequence of previously visited venues while not taking into consideration the contextual information related to the check-ins. Thus, its prediction accuracy is lower than that of the hybrid models. However, it should be stated that the performance of these hybrid approaches was not worse than that of RNN and LSTM. Thus, it is worth modeling geographical and spatial contextual information for the task of the next POI recommendations. It means that it is insufficient to have a good network architecture, but more geographical and spatial contextual information of human check-in behaviors should be taken into account to obtain excellent results [1]. This is the reason for the outperformance of CARA over DRCF.
The accuracy prediction in the CARA model is higher compared to other models due to the separate use of TCI and GCI and a combination of the RNN, attention, and factoring approaches. Inspired by the idea behind this model, the researchers introduced a new initiative to employ three gates in the GRU model to address GCI and TCI to better predict the accuracy of the next POI recommendation. As mentioned in previous sections, the proposed model uses three separate attention gates, namely, Gts, Gge, and Gte, which consider the timestamp, geographical distance, and time interval between successive check-ins, respectively, and the output of each of them separately affects the values of the reset and update gates of the GRU model. As depicted in Fig. 3, the experiment results of the proposed models indicate that it has achieved this goal, and the accuracy prediction has been improved in the proposed GTCI-EAGRU architecture.
Fig. 3. Comparison of GTCI-EAGRU with baseline methods in terms of Accuracy@10 on three datasets
To answer RQ1 for the development of the GRU model, it should be mentioned that three gates were introduced and implemented as a feed-forward network. The output of these gates affects the values of the GRU reset and update gates, and they are responsible for controlling the geographical and temporal information of the user’s trajectory data. To answer RQ2 and RQ3, these results were obtained (Table 6) by comparing the accuracy prediction of the GTCI-EAGRU model with up-to-date architectures.
Table 6. Percentage of Improvement of GTCI-EAGRU
Methods | Percentage of Improvement | |||
Brightkite (%) | Gowalla (%) | Foursquare (%) | ||
STGN | 79.28 | 45.54 | 66.10 | |
ARNN | - | 75.68 | 51.86 | |
GeoSAN | 34.11 | 37.25 | 45.32 | |
DRCF | 24.49 | - | 01.08 | |
CARA | 24.26 | - | 00.56 | |
Improvement | 40.54 | 52.82 | 32.98 | |
On Average in three Dataset |
|
6- Conclusions
In recent years, the next POI recommendation is of great importance for a wide spectrum of LBSN applications. The influences of contextual information (e.g., spatial and temporal context information) are crucial for analyzing individual behaviors for personalized POI recommendations. Hence, many studies have considered this contextual information to improve the performance of POI recommendation algorithms such as the CF and RNN. There are still many challenges regarding how to integrate contextual information to accurately model users’ complex behavioral preferences and recommend reliable POIs to users.
The current study proposed a novel GTCI-EAGRU for the next POI recommendation by addressing the challenges concerning previous studies. Our proposed architecture was presented with the development of the GRU model, in which the contextual information of the user trajectory data is considered separately. Moreover, the development of the model inspired by the AM makes contextual information more important in modeling sequential user data. POIs were scored to provide recommendations to a user from her/his historical check-ins. The simple development of this model for considering more contextual information is one of the other features of the proposed model.
By comparing the experimental results of baseline methods, an increase in the accuracy of prediction indicates the importance of considering contextual information separately. The proposed GTCI-EAGRU architecture with three additional contextual attention gates worked well for the next POI recommendation.
In this study, the comprehensive experiments conducted on three large- scale datasets from the Brightkite, Gowalla, and Foursquare demonstrated a significant improvement in the GTCI-EAGRU architecture for the next POI recommendations compared with various up-to-date recurrent architectures and many different recent factorization approaches.
To enhance the quality of recommendations for the next POI, the GTCI-EAGRU architecture could be enriched by adding the impact of each user’s social relationships with other users on LBSNs. Furthermore, it can be possible to include more contextual information (e.g., visual and text information) related to users’ check-ins or the weather condition of the check-in registration location as well.
Acknowledgment
This manuscript is prepared based on PhD. thesis of the first author at Rasht Branch, Islamic Azad University, Rasht, Iran.
References
[1] L. Huang, Y. Ma, Sh. Wang, Y. Liu, “An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation”, Journal of IEEE Transactions on Services Computing, vol. 12, 2019, pp. 1-13.
[2] J. Manotumruksa, C. Macdonald, I. Ounis, “A Contextual Attention Recurrent Architecture for Context- Aware Venue Recommendation”, in 18th ACM SIGIR Conference on Research and Development in Information Retrieval, 2018, pp. 555-564.
[3] K. Kala, M. Nandhini,” Context Category Specific sequence aware Point of Interest Recommender System with Multi Gated Recurrent Unit”, Journal of Ambient Intelligence and Humanized Computing, 2019, https://doi.org/10.1007/s12652-019-01583-w.
[4] C. Liu, J. Liu, J. Wang, S. Xu, H. Han, Y. Chen, “An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation”, International Journal of Geo-Information, vol. 8, No. 8, 2019, pp.355-373.
[5] S. Wang, Z. Bao, J. Culpepper, G. Cong, “A Survey on Trajectory Data Management, Analytics, and Learning”, ACM Computing Surveys, vol. 54, No.3, 2020, pp. 1-33.
[6] J. Feng Y. Li, C. Zhang, F. Sun, F. Meng, A. Guo, D. Jin, “DeepMove: Predicting Human Mobility with Attentional Recurrent Networks”, in 18th ACM IW3C2 Conference on International World Wide Web, 2018, pp. 1459-1468.
[7] Q. Guo, Z .Sun, J. Zhang, Y. Theng, “An Attentional Recurrent Neural Network for Personalized Next Location Recommendation”, in 34th ACM AAAI Conference on Artificial Intelligence, 2020, pp. 83-90.
[8] D.Yao,C. Zhang, J. Huang, J. Bi, “SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories”, in 17th ACM CIKM Conference on Information and Knowledge Management, 2017 ,pp. 2411-2414.
[9] L. Zhang, Z. Sun, J. Zhang, H. Kloeden, F. Klanner, “Modeling hierarchical category transition for next POI recommendation with uncertain check-ins”, Journal of Information Sciences, Elsevier, vol.515, 2019, pp. 169-190.
[10] L. Chang, W. Chen, J. Huang, Ch. Bin, W. Wang, “Exploiting multi-attention network with contextual influence for point-of-interest recommendation”, Journal of Applied Intelligence, vol. 51, 2021, pp. 1904–1917.
[11] L. Huang, Y. Ma, Y. Liu, K. He, “DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation”, Journal of ACM Transactions on Internet Technology, Vol.21, No.2, 2020, pp. 1–27.
[12] G. Christoforidis, P. Kefalas, A. Papadopoulos, Y. Manolopoulos, “RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings”, Journal of Knowledge and Information Systems, Vol. 63, No.4, 2019, pp. 791-817.
[13] J. Manotumruksa, C. Macdonald, I. Ounis, “A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation”, in 17th ACM CIKM Conference on Information and Knowledge Management, 2017, pp. 1429-1438.
[14] D. Yang, D Zhang, V. Zheng, Z. Yu, “Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs”, Journal of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.45, No.1, 2014, pp. 129 – 142.
[15] M. Quadrana, P. Cremonesi, D. Jannach, “Sequence-Aware Recommender Systems”, Journal of ACM Computing Surveys, Vol.51, No.4, 201, pp. 1–36.
[16] Q. Cui, Y. Tang, S. Wu, L. Wang, “Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction”, in PAKDD Conference on Knowledge Discovery and Data Mining, 2019, pp. 289-301.
[17] Q. Gao, F. Zhou, G. Trajcevski, K. Zhang, T. Zhong, F. Zhang, “Predicting Human Mobility via Variational Attention”, in IW3C2 Conference on International World Wide Web Conference Committee, 2019, pp. 2750–2756.
[18] A.Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need”, in 31th NIPS Conference on Neural Information Processing System, 2017, pp. 5998-6008.
[19] Y. Chen, C. Long, G.Cong, C. Li, “Context-aware Deep Model for Joint Mobility and Time Prediction”,in 13th ACM WSDM Conference on Web Search and Data Mining, 2020, pp. 106-114.
[20] S. Rendle, C. Freudenthaler, Z. Gantner, L. Thieme, “BPR: Bayesian Personalized Ranking from Implicit Feedback”, in 25th ACM UAI Conference on Uncertainty in Artificial Intelligence, 2009, pp. 452–461.
[21] E.Cho, S. Myers, J. Leskovec, “Friendship and Mobility: User Movement in Location-Based Social Networks”, in 17th ACM KDD Conference on Knowledge Discovery and Data Mining, 2011, pp. 1082–1090.
[22] P. Zhao, H. Zhu, Y. Liu, J. Xu, F. Zhuang, V. Sheng, X. Zhou, “Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation”,. in 33th AAAI Conference on Artificial Intelligence, 2019, pp. 5877-5884.
[23] A M. Islam, M. M. Mohammad, S. S. Das, M. E. Ali, “A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations”, 2020, arXiv:2011.10187v1.
[24] C. Zheng, D. Tao, “Attention-Based Dynamic Preference Model for Next Point-of-Interest Recommendation”. in 15th Springer WASA Conference on Wireless Algorithms, Systems, and Applications, 2020, pp. 768–780.
[25] D. K. Bokde, Sh. Girase, D. Mukhopadhyay, “Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey”, International Journal of Advance Foundation and Research in Computer, vol.1, 2014, pp. 111-118.
[26] M. Gan, L. Gao, “Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks”, International Journal of Geo-Information (IJGI), Vol.8, No.6, 2019, pp. 279-294.
[27] X. Meng, J. Fang, “A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations”, Journal of IEEE Access, Vol.8, 2020, pp. 226309- 226323.
[28] Q. Yuan, G. Cong, Z. Ma, A. Sun, N. Thalmann, “Time-aware Point-of-interest Recommendation”, in 36th ACM SIGIR Conference on Research and development in Information Retrieval, 2013, pp. 363–372.
[29] P. Wang, H. Wang, H. Zhang, F. Lu, S. Wu, “A Hybrid Markov and LSTM Model for Indoor Location Prediction”, Journal of IEEE Access, Vol.7, 2019, pp. 185928 – 185940.
[30] J. Li, G. Liu, C. Yan, C. Jiang, “LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation”, IEEE Transactions on Computational Social Systems, Vol.6, No.3, 2019, pp. 430 – 440.
[31] L.Yao, Q. Z. Sheng, Y. Qin, X. Wang, A. Shemshadi, Q. He, “Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization”, in 38th.ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 1007–1010.
[32] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T. Chua, “Neural collaborative filtering”, in 26th ACM IW3C2Conference on World Wide Web Conference Committee, 2017, pp. 173-182.
[33] Q. Liu, S.Wu, L. Wang, T. Tan, “Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts”, in 30th ACM AAAI Conference, 2016 , pp. 194–200.
[34] S. Kumar, M.I. Nezhurina, “An ensemble classification approach for prediction of user’s next location based on Twitter data”, Journal of Ambient Intelligence and Humanized Computing, Vol.10, No. 11, 2018, pp. 4503-4513.
[35] Q. Liu, S. Wu, D. Wang, Z. Li, L. Wang, “Context-Aware Sequential Recommendation”, in ICDM Conference on Data Mining, IEEE, 2016, pp. 1053-1058.
[36] D. Bokde, S. Girase, D. Mukhopadhya, “Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey”, Procedia Computer Science, Vol.49, 2015, pp. 136-146.
[37] D. Lian, Y. Wu, Y. Ge, X. Xie, E. Chen, “Geography-Aware Sequential Location Recommendation”, in 26th ACM ICGKDD Conference on Knowledge Discovery and Data Mining, 2020, pp. 2009–2019.
[38] K. Yang, J. Zhu, “Next POI Recommendation via Graph Embedding Representation from H-Deepwalk on Hybrid Network”, Journal of IEEE Access, Vol 7, 2019, pp. 171105 – 171113.
[39] D. P. Kingma, J.L. Ba, “A Method for Stochastic Optimization”, in International Conference for Learning Representations, 2015, arXiv:1412.6980v.
* Gholamhossein Ekbatanifard
ekbatanifard@liau.ac.ir
[1] https://scikit-learn.org.
[2] In general, the Eq. (10) is used to calculate the Haversine distance between samples in X and Y (x1 and x2 are latitude and longitude of X and y1 and y2 are latitude and longitude of Y, respectively).
[3] https://snap.stanford.edu/data/loc-brightkite.html
[4] https://snap.stanford.edu/data/loc-gowalla.html
[5] https://sites.google.com/site/yangdingqi/home/foursquare-dataset