DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback
محورهای موضوعی : Data Mining
Maral Kolahkaj
1
(Islamic Azad University, Soosangerd)
Ali Harounabadi
2
(Islamic Azad university, Central Tehran Branch)
Alireza Nikravan shalmani
3
(Karaj Branch, Islamic Azad University, Karaj, Iran )
Rahim Chinipardaz
4
(University of Shahid Chamran, Ahwaz, Iran)
کلید واژه: Decision Support Systems, , Data Mining, , Context-aware Recommendation, , Geo-tagged Photo, , Asymmetric Similarity, ,
چکیده مقاله :
By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures.
By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures.
[1] D. Godoy and A. Corbellini, "Folksonomy-Based Recommender Systems: A State-of-the-Art Review," Int. J. Intell. Syst., vol. 31, no. 4, pp. 314-346, 2016.
[2] P. Yu, L. Lin, and Y. Yao, "A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems," in Proceedings of the 17th International Conference on Web-Age Information Management, WAIM 2016, Nanchang, China, Part I, 2016, pp. 231-243.
[3] Z. K. Zhang, T. Zhou, and Y. C. Zhang, "Tag-Aware Recommender Systems: A State-of-the-Art Survey," Journal of Computer Science and Technology, vol. 26, no. 5, pp. 767-777, 2011.
[4] Z. L. Zhao, C. D. Wang, Y. Y. Wan, and J. H. Lai, "Recommendation in feature space sphere," Electronic Commerce Research and Applications, vol. 26, no. Supplement C, pp. 109-118, 2017.
[5] S. Khusro, Z. Ali, and I. Ullah, "Recommender Systems: Issues, Challenges, and Research Opportunities," in Proceedings of the Information Science and Applications (ICISA), Singapore, 2016, pp. 1179-1189.
[6] M. Kolahkaj, A. Harounabadi, and M. Sadeghzade, "A Recommender System for Web Mining using Neural Network and Fuzzy Algorithm," International Journal of Computer Applications, vol. 78, no. 8, pp. 20-24, 2013.
[7] M. Kolahkaj and M. Khalilian, "A recommender system by using classification based on frequent pattern mining and J48 algorithm," in 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015, pp. 780-786.
[8] R. Safa, S. Mirroshandel, S. Javadi, and M. Azizi, "Venue Recommendation Based on Paper’s Title and Co-authors Network," Journal of Information Systems and Telecommunication, vol. 1, no. 6, pp. 209-217, 2017.
[9] X. Ma, H. Lu, Z. Gan, and J. Zeng, "An explicit trust and distrust clustering based collaborative filtering recommendation approach," Electronic Commerce Research and Applications, vol. 25, no. Supplement C, pp. 29-39, 2017.
[10] R. Gao, J. Li, X. Li, C. Song, and Y. Zhou, "A personalized point-of-interest recommendation model via fusion of geo-social information," Neurocomputing, vol. 273, pp. 159-170, 2018.
[11] Y. M. Afify, I. F. Moawad, N. L. Badr, and M. F. Tolba, "A personalized recommender system for SaaS services," Concurrency and Computation: Practice and Experience, vol. 29, no. 4, p. e3877, 2017.
[12] N. M. Villegas, C. Sánchez, J. Díaz-Cely, and G. Tamura, "Characterizing context-aware recommender systems: A systematic literature review," Knowledge-Based Systems, vol. 140, pp. 173-200, 2018.
[13] I. Cenamor, T. de la Rosa, S. Núñez, and D. Borrajo, "Planning for tourism routes using social networks," Expert Systems with Applications, vol. 69, pp. 1-9, 2017.
[14] G. Cai, K. Lee, and I. Lee, "Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos," Expert Systems with Applications, vol. 94, pp. 32-40, 2018.
[15] I. Memon, L. Chen, A. Majid, M. Lv, I. Hussain, and G. Chen, "Travel Recommendation Using Geo-tagged Photos in Social Media for Tourist," Wirel. Pers. Commun., vol. 80, no. 4, pp. 1347-1362, 2015.
[16] D. Wang, S. Deng, and G. Xu, "Sequence-based context-aware music recommendation," Information Retrieval Journal, vol. 21, no. 2, pp. 230-252, 2018.
[17] D. Bachmann et al., "(CF)2 architecture: contextual collaborative filtering," Information Retrieval Journal, 2018. [18] Z. Xiang and U. Gretzel, "Role of social media in online travel information search," Tourism Management, vol. 31, no. 2, pp. 179-188, 2010.
[19] V. Subramaniyaswamy, V. Vijayakumar, R. Logesh, and V. Indragandhi, "Intelligent Travel Recommendation System by Mining Attributes from Community Contributed Photos," Procedia Computer Science, vol. 50, pp. 447-455, 2015.
[20] R. S. Aquino, H. A. Schänzel, and K. F. Hyde, "Unearthing the geotourism experience: Geotourist perspectives at Mount Pinatubo, Philippines," Tourist Studies, vol. 18, no. 1, pp. 41-62, 2018.
[21] L. Ravi and S. Vairavasundaram, "A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users," Intell. Neuroscience, vol. 2016, p. 7, 2016.
[22] M. Pazzani and D. Billsus, "Learning and Revising User Profiles: The Identification ofInteresting Web Sites," Mach. Learn., vol. 27, no. 3, pp. 313-331, 1997.
[23] D. H. Park, H. K. Kim, I. Y. Choi, and J. K. Kim, "A literature review and classification of recommender systems research," Expert Systems with Applications, vol. 39, no. 11, pp. 10059-10072, 2012.
[24] M. Nilashi, O. bin Ibrahim, N. Ithnin, and N. H. Sarmin, "A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS," Electronic Commerce Research and Applications, vol. 14, no. 6, pp. 542-562, 2015.
[25] G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005.
[26] M. Balabanovi and Y. Shoham, "Fab: content-based, collaborative recommendation," Commun. ACM, vol. 40, no. 3, pp. 66-72, 1997.
[27] W. Wu et al., "Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding," Knowledge-Based Systems, vol. 128, pp. 71-77, 2017.
[28] D. H. Lee and P. Brusilovsky, "Improving personalized recommendations using community membership information," Information Processing & Management, vol. 53, no. 5, pp. 1201-1214, 2017.
[29] T. Ha and S. Lee, "Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network," Information Processing & Management, vol. 53, no. 5, pp. 1171-1184, 2017.
[30] A. Sesagiri Raamkumar, S. Foo, and N. Pang, "Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems," Information Processing & Management, vol. 53, no. 3, pp. 577-594, 2017.
[31] K. H. L. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme, "Tag-aware recommender systems by fusion of collaborative filtering algorithms," in Proceedings of the ACM symposium on Applied computing, Fortaleza, Ceara, Brazil, 2008, pp. 1995-1999.
[32] A. M. Nagrale and A. P. Pande, "User Preferences-based Recommendation System for Services Using Map Reduce Approach for Big Data Applications," International Journal of Innovations & Advancement in Computer Science, vol. 4, pp. 528-532, 2015.
[33] M. Y. H. Al-Shamri, "Effect of Collaborative Recommender System Parameters," Adv. in Artif. Intell., vol. 2016, pp. 1-10, 2016.
[34] Z. Zhang, X. Zheng, and D. D. Zeng, "A framework for diversifying recommendation lists by user interest expansion," Knowledge-Based Systems, vol. 105, pp. 83-95, 2016.
[35] J. Bobadilla, F. Ortega, A. Hernando, and J. Alcalá, "Improving collaborative filtering recommender system results and performance using genetic algorithms," Knowledge-Based Systems, vol. 24, no. 8, pp. 1310-1316, 2011.
[36] Q. Cheng et al., "The new similarity measure based on user preference models for collaborative filtering," in Proceedings of the IEEE International Conference on Information and Automation, 2015, pp. 577-582.
[37] G. Guo, J. Zhang, and N. Yorke-Smith, "A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems," ACM Trans. Web, vol. 10, no. 2, pp. 1-30, 2016.
[38] M. Y. H. Al-Shamri, "User profiling approaches for demographic recommender systems," Knowledge-Based Systems, vol. 100, pp. 175-187, 2016.
[39] S. Zammali, K. Arour, and A. Bouzeghoub, "A Context Features Selecting and Weighting Methods for Context-Aware Recommendation," in Proceedings of the IEEE 39th Annual Computer Software and Applications Conference, 2015, vol. 2, pp. 575-584.
[40] H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, "A new user similarity model to improve the accuracy of collaborative filtering," Knowledge-Based Systems, vol. 56, pp. 156-166, 2014.
[41] Z. L. Zhao, C. D. Wang, and J. H. Lai, "AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value," PLoS ONE, vol. 11, no. 2, pp. 1-21, 2016.
[42] S. H. Cha, "Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions," International Journal of Mathematical Models and Methods in Applied Sciences, vol. 1, no. 4, pp. 300-307, 2007.
[43] P. Pirasteh, D. Hwang, and J. E. Jung, "Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering," Mob. Netw. Appl., vol. 20, no. 4, pp. 497-507, 2015.
[44] A. Majid, L. Chen, G. Chen, H. T. Mirza, I. Hussain, and J. Woodward, "A context-aware personalized travel recommendation system based on geotagged social media data mining," Int. J. Geogr. Inf. Sci., vol. 27, no. 4, pp. 662-684, 2013.
[45] Y. Zheng, R. Burke, and B. Mobasher, "Recommendation with Differential Context Weighting," in Proceedings of the 21th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2013, Rome, Italy, 2013, pp. 152-164.
[46] P. Pirasteh, J. J. Jung, and D. Hwang, "An Asymmetric Weighting Schema for Collaborative Filtering," in In: Proceedings of the New Trends in Computational Collective Intelligence, 2015, pp. 77-82: Springer.
[47] P. Pirasteh, D. Hwang, and J. J. Jung, "Exploiting matrix factorization to asymmetric user similarities in recommendation systems," Knowledge-Based Systems, vol. 83, pp. 51-57, 2015.
[48] B. K. Patra, R. Launonen, V. Ollikainen, and S. Nandi, "A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data," Know.-Based Syst., vol. 82, no. C, pp. 163-177, 2015.
[49] S. Vairavasundaram, V. Varadharajan, I. Vairavasundaram, and L. Ravi, "Data mining-based tag recommendation system: an overview," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 5, no. 3, pp. 87-112, 2015.
[50] G. Adomavicius and A. Tuzhilin, "Context-Aware Recommender Systems," in Recommender Systems Handbook1st ed.: Springer US, 2011, pp. 217-253.
[51] A. Majid, L. Chen, H. T. Mirza, I. Hussain, and G. Chen, "A system for mining interesting tourist locations and travel sequences from public geo-tagged photos," Data & Knowledge Engineering, vol. 95, pp. 66-86, 2015.
[52] Suryakant and T. Mahara, "A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment," Procedia Computer Science, vol. 89, pp. 450-456, 2016.
[53] Z. Xu, "Trip similarity computation for context-aware travel recommendation exploiting geotagged photos," in Proceedings of the 30th IEEE International Conference on Data Engineering Workshops, 2014, pp. 330-334.
[54] S. Kisilevich, F. Mansmann, and D. Keim, "P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos," in Proceedings of the 1st ACM International Conference and Exhibition on Computing for Geospatial Research & Application, Washington, D.C., USA, 2010, pp. 1-4.
[55] L. J. Li, R. K. Jha, B. Thomee, D. A. Shamma, L. Cao, and Y. Wang, "Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos," in Proceeding of the Large-Scale Visual Geo-Localization, 2016, pp. 41-58.
[56] M. Ester, H. P. Kriegel, r. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 1996, pp. 226-231.
[57] J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," Third ed. Boston: Morgan Kaufmann, 2012, pp. 39-82.
[58] B. Thomee et al., "YFCC100M: the new data in multimedia research," Commun. ACM, vol. 59, no. 2, pp. 64-73, 2016.
[59] Flickr. (2018, 6 jan. 2017). Available: http://www.Flickr.com
[60] WebscopeYahooLabs. (15 Feb 2017). Available: https://webscope.sandbox.yahoo.com/catalog.php?datatype=i&did=67
[61] A. Bellogín and P. Sánchez, "Collaborative filtering based on subsequence matching: A new approach," Information Sciences, vol. 418-419, pp. 432-446, 2017.
