List of Articles Recommender system

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  1 - IMPROVE THE RECOMMENDER SYSTEM USING SEMANTIC WEB
  mohammad ebrahim samie
  To buy his/her necessities such as books, movies, CD, music, etc., one always trusts others’ oral and written consultations and offers and include them in his/her decisions. Nowadays, regarding the progress of technologies and development of e-business in websites, a ne More

  To buy his/her necessities such as books, movies, CD, music, etc., one always trusts others’ oral and written consultations and offers and include them in his/her decisions. Nowadays, regarding the progress of technologies and development of e-business in websites, a new age of digital life has been commenced with the Recommender systems. The most important objectives of these systems include attracting the customers and their confidences by detecting their interests and tastes and recommending them the most appropriate offer. Using the relationships between entities in the DBpedia ontology, this study tries to investigate the application of and extracting the information in the movie area. In the next step, the structure of Recommender System is designed and its performance is evaluated using information extracted from "MovieLens" database. This study’s endeavor is to present a comprehensive overview of Recommender systems and a proposed method based on the benefits of semantic web databases, along with the implementation compared to existing methods. Our results indicate that the proposed method outperforms in terms of efficiency and performance. Manuscript profile
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  2 - Model of customer loyalty in e-commerce recommender systems
  Leila Ebrahimi
  The main goal of this study is provide a customer loyalty model for e-commerce recommender system. In this research, a new model of customer loyalty is presented for e-commerce recommender system and has been empirically tested, which could allow e-businesses to adopt a More

  The main goal of this study is provide a customer loyalty model for e-commerce recommender system. In this research, a new model of customer loyalty is presented for e-commerce recommender system and has been empirically tested, which could allow e-businesses to adopt appropriate strategies for maintaining and keeping electronic customers up to date. This research is in terms of applied purpose, and in terms of the nature and method of descriptive-correlation method. The data needed for statistical analysis of the research was obtained by simple random sampling and by distributing the questionnaire among 384 users who had the experience of purchasing e-commerce from websites (Digikala) with a recommender system .Structural equation modeling and path analysis techniques were used using Smart PLS software to investigate research questions and examine the relationships between variables. The result of structural equation modeling suggests that all of the indices in the research model are confirmed and the research model has a suitable fit. Manuscript profile
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  3 - context-aware travel recommender system exploiting from Geo-tagged photos
  rezvan mohamadrezaei larki Reza Ravanmehr milad  amrolahi
  Recommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, locatio More

  Recommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, location . This paper is presented by the use of geographically tagged photo information which is highly accurate. The distinction point between this thesis and other similar articles is that this paper includes more context (weather conditions, users’ mental status, traffic level, etc.) than similar articles which include only time and location as context. This has brought the users close to each other in a cluster and has led to an increase in the accuracy. The proposed method merges the Colonial Competitive Algorithm and fuzzy clustering for a better and stronger processing against using merely the classic clustering and this has increased the accuracy of the recommendations. Flickr dataset is used to evaluate the presented method. Results of the evaluation indicate that the proposed method can provide location recommendations proportionate to the users’ preferences and their current visiting location. Manuscript profile
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  4 - Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
  Ramin Safa Seyed Abolghassem Mirroshandel Soroush Javadi Mohammad Azizi
  10.7508/jist.2018.21.005
  Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying More

  Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations. Manuscript profile
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  5 - User recommendation in Telegram messenger by graph analysis and mathematical modeling of users' behavior
  Davod Karimpour Mohammad Ali Zare Chahooki Ali Hashemi
  Recommender systems on social networks and websites have been developed to reduce the production and processing of queries. The purpose of these systems is to recommend users various items such as books, music, and friends. Users' recommendation on social networks and i More

  Recommender systems on social networks and websites have been developed to reduce the production and processing of queries. The purpose of these systems is to recommend users various items such as books, music, and friends. Users' recommendation on social networks and instant messengers is useful for users to find friends and for marketers to find new customers. On social networks such as Facebook, finding target users for marketing is an integrated feature, but in instant messengers such as Telegram and WhatsApp, it is not possible to find the target community. In this paper, by using graph and modeling the intergroup behavior of users and also defining features related to groups, a method for recommending Telegram users has been presented. The proposed method consists of 8 steps and each step can be considered a separate method for user recommendation. The data used in this paper is a real data set including more than 900,000 supergroups and 120 million Telegram users crawled by the Idekav system. Evaluation of the proposed method on high-quality groups showed an average reduction in error by 0.0812 in RMSE and 0.128 in MAE. Manuscript profile
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  6 - Adaptive Educational Hypermedia Web Pages Recommending Based on Learning Automata and User Feedback for Resource-Based Learning
  Mohammad Tahmasebi Faranak Fotouhi-Dgazvini M. Esmaeili
  Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a persona More

  Personalized recommender systems and search engines, are two effective key solutions to overcome the information overloading problem. They use the intelligent techniques on users’ interactions to extract their behavioral patterns. These patterns help in making a personalized environment to deliver accurate recommendations. In the technology enhanced learning (TEL) field and in particular resource-based learning, recommendation systems have attracted growing interest. Specially, they are an important module of Adaptive Educational Systems that deliver the appropriate learning objects to their users. Gray-sheep users are a challenge in these systems. They have a little similarity with their peers. So the recommendations to others are not suitable for them. To overcome this problem, we propose the idea of accommodating the user’s learning style to web page features. The user's learning style will be computed according to Felder-Silverman theory. On the other hands, the necessary implicit and explicit meta data will be extracted from OCW web pages. By matching the extracted information, the system delivers the appropriate educational links to user. The user can compare the proposed links, based of our algorithm, to the output of Lucene algorithm. A user’s opinion about every web page as a recommended result would be submitted to the system. The system uses a learning automata algorithm and user’s feedback to deliver best recommendations. The system has been evaluated by a group of engineering students to evaluate its accuracy. Results show that the proposed method outperforms the general search algorithm. This system can be used at formal and informal learning and educational environments for Resource-based learning. Manuscript profile
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  7 - Improving Precision of Recommender Systems using Time-, Location- and Context-aware Trust Estimation Based on Clustering and Beta Distribution
  Samaneh Sheibani Hassan Shakeri Reza Sheybani
  Calculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimat More

  Calculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimating trust among users of recommender systems is introduced. In the proposed scheme, first, users of the system are clustered based on their similarities in demographics information and history of ratings. To predict the rating of the active user into a specific item, the value of trust between him and the other users in his cluster is calculated considering the factors i.e. time, location, and context of their rating. To this end, we propose an algorithm based on beta distribution. A novel tree-based measure for computing the semantic similarity between the contexts is utilized. Finally, the rating of the active user is predicted using weighted averaging where trust values are considered as weights. The proposed scheme was performed on three datasets, and the obtained results indicated that it outperforms existing methods in terms of accuracy and other efficiency metrics. Manuscript profile
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  8 - A Recommender System for Scientific Resources Based on Recurrent Neural Networks
  Hadis Ahmadian Seyed Javad  Mahdavi Chabok Maryam  Kheirabadi
  10.61186/jist.39439.11.44.282
  Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses direct More

  Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses directly affect the number of active participants; on the other hand, it becomes difficult for teachers to guide students in choosing the appropriate learning resource due to the abundance of online learning resources. Students also find it challenging to decide which educational resources to choose according to their condition. The resource recommender system can be used as a Guide tool for educational resource recommendations to students so that these suggestions are tailored to the preferences and needs of each student. In this paper, it was presented a resource recommender system with the help of Bi-LSTM networks. Utilizing this type of structure involves both long-term and short-term interests of the user and, due to the gradual learning property of the system, supports the learners' behavioral changes. It has more appropriate recommendations with a mean accuracy of 0.95 and a loss of 0.19 compared to a similar article. Manuscript profile
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  9 - A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
  Mohammad Javad shayegan mohadeseh valizadeh
  <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of perso More

  <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.</span></p> Manuscript profile
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  10 - Presenting a web recommender system for user nose pages using DBSCAN clustering algorithm and machine learning SVM method.
  reza molaee fard Mohammad mosleh
  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can s More

  Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent. Manuscript profile