International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Double Blind Reviewed

ISSN: 2319-7064




Downloads: 131

Research Paper | Computer Science & Engineering | India | Volume 6 Issue 7, July 2017


Social Recommendation Model Regularized with User Trust and Item Ratings

Dr. G. Anandharaj | M. Anu [4]


Abstract: Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Recommender systems have been widely used to provide users with high-quality personalized recommendations from a large volume of choices. Robust and accurate recommendations are important in e-commerce operations (e. g. , navigating product offerings, personalization, improving customer satisfaction), and in marketing (e. g. , tailored advertising, segmentation, cross-selling). The former issue refers to the fact that users usually rate only a small portion of items while the latter indicates that new users only give a few ratings (a. k. a. cold-start users). Both issueseverely degrade the efficiency of a recommender system in modeling user preferences and thus the accuracy of predicting a users rating for an unknown item.


Keywords: Social trust, implicit ratings, explicit ratings, e-commerce


Edition: Volume 6 Issue 7, July 2017,


Pages: 839 - 844


How to Cite this Article?

Dr. G. Anandharaj, M. Anu, "Social Recommendation Model Regularized with User Trust and Item Ratings", International Journal of Science and Research (IJSR), Volume 6 Issue 7, July 2017, pp. 839-844, https://www.ijsr.net/get_abstract.php?paper_id=ART20174948

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