Improving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data | ||
| Journal of AI and Data Mining | ||
| مقاله 7، دوره 8، شماره 3، مهر 2020، صفحه 379-389 اصل مقاله (851.63 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.7326.1871 | ||
| نویسندگان | ||
| B. Hassanpour1؛ N. Abdolvand* 2؛ S. Rajaee Harandi2 | ||
| 1Department of Electrical, Computer and IT Engineering, Qazvin Islamic Azad University, Qazvin, Iran. | ||
| 2Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran. | ||
| چکیده | ||
| The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of user interests, it seems reasonable to combine it with social networks information and the factor of time. Hence, this study, for the first time, tries to assess the efficiency of the recommender systems by combining fuzzy logic, longitudinal data and social networks information such as tags, friendship, and membership in groups. And the impact of the proposed algorithm for improving the accuracy of recommender systems was studied by specifying the neighborhood and the border between the users’ preferences over time. The results revealed that using longitudinal data in social networks information in memory-based recommender systems improves the accuracy of these systems. | ||
| کلیدواژهها | ||
| Recommender System؛ Social Network؛ Longitudinal Data؛ Fuzzy Logic؛ Tags | ||
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آمار تعداد مشاهده مقاله: 691 تعداد دریافت فایل اصل مقاله: 667 |
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