Intelligent Online Store: User Behavior Analysis based Recommender System
Recommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful te...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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University of Tehran
2015-06-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_53884_4a365079a618ddd9ac5aa8c933f8ce05.pdf |
| _version_ | 1818530714856980480 |
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| author | Mohamadreza Karimi Alavije Shiva Askari Sirvan Parasite |
| author_facet | Mohamadreza Karimi Alavije Shiva Askari Sirvan Parasite |
| author_sort | Mohamadreza Karimi Alavije |
| collection | DOAJ |
| description | Recommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems facilitating the provision of recommendations close to that of the customer's taste and need. However the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users. |
| first_indexed | 2024-12-11T17:23:13Z |
| format | Article |
| id | doaj.art-32b1417d364e42e8a6c9ce55fe4ee73b |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-12-11T17:23:13Z |
| publishDate | 2015-06-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-32b1417d364e42e8a6c9ce55fe4ee73b2022-12-22T00:57:03ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592015-06-017238540610.22059/jitm.2015.5388453884Intelligent Online Store: User Behavior Analysis based Recommender SystemMohamadreza Karimi Alavije0Shiva Askari1Sirvan Parasite2MSc, Artifiacial Intelligent, Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, IranAssistant Prof., Business Management, Faculty of Management and Accounting University of Allameh Tabatabayi, Tehran, IranMSc Student, Business Management, Faculty of Management and Accounting, Allameh Tabatabayi University, Tehran, IranRecommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems facilitating the provision of recommendations close to that of the customer's taste and need. However the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users.https://jitm.ut.ac.ir/article_53884_4a365079a618ddd9ac5aa8c933f8ce05.pdfClusteringCollaborative filteringdemographicsRecommender System |
| spellingShingle | Mohamadreza Karimi Alavije Shiva Askari Sirvan Parasite Intelligent Online Store: User Behavior Analysis based Recommender System Journal of Information Technology Management Clustering Collaborative filtering demographics Recommender System |
| title | Intelligent Online Store: User Behavior Analysis based Recommender System |
| title_full | Intelligent Online Store: User Behavior Analysis based Recommender System |
| title_fullStr | Intelligent Online Store: User Behavior Analysis based Recommender System |
| title_full_unstemmed | Intelligent Online Store: User Behavior Analysis based Recommender System |
| title_short | Intelligent Online Store: User Behavior Analysis based Recommender System |
| title_sort | intelligent online store user behavior analysis based recommender system |
| topic | Clustering Collaborative filtering demographics Recommender System |
| url | https://jitm.ut.ac.ir/article_53884_4a365079a618ddd9ac5aa8c933f8ce05.pdf |
| work_keys_str_mv | AT mohamadrezakarimialavije intelligentonlinestoreuserbehavioranalysisbasedrecommendersystem AT shivaaskari intelligentonlinestoreuserbehavioranalysisbasedrecommendersystem AT sirvanparasite intelligentonlinestoreuserbehavioranalysisbasedrecommendersystem |