Publication Type

Conference Proceeding Article

Publication Date

4-2017

Abstract

Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in social relations originated from social sciences, and then propose anovel social recommendation method based on a new Probabilistic Matrix Factorization model that incorporates the distinction of strong and weak ties for improving recommendation performance. The proposed method is capable of simultaneously classifying different types of social ties in a social network w.r.t. optimal recommendation accuracy, and learning a personalized tie type preference for each user in addition to other parameters. We conduct extensive experiments on four real-world datasets by comparing our method with state-of-the-art approaches, and find encouraging results that validate the efficacy of the proposed method in exploiting the personalized preferences of strong and weak ties for social recommendation.

Keywords

Social Recommendation, Personalization, Strong and Weak Ties, User Behavior Modeling

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Management and Analytics

Publication

WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion

First Page

1601

Last Page

1610

ISBN

978-1-4503-4913-0

Identifier

10.1145/3038912.3052556

Publisher

ACM

City or Country

New York, USA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/3038912.3052556

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