Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

Abstract

Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two challenges that fail to be solved by existing methods appear: i) how to develop a mathematical model which can optimally choose a subset of friends for each user such that these friends' preferences can best influence the target user, and ii) how can the model learn an optimal attention for each of these selected friends. To tackle these challenges, we first propose to formulate the problem of optimal limited attention in social recommendation. We then develop a novel algorithm through employing an EM-style strategy to jointly optimize users' latent preferences, optimal number of their best influential friends and the corresponding attentions. We also give a rigorous proof to guarantee the algorithm's optimality. The proposed model is capable of efficiently finding an optimal number of friends whose preferences have the best impact on target user as well as adaptively learning an optimal personalized attention towards every selected friend w.r.t. the best recommendation accuracy. Extensive experiments on real-world datasets demonstrate the superiority of our proposed model over several state-of-the-art algorithms.

Keywords

Recommendation, User Behavior Modeling, Limited Attention

Discipline

Databases and Information Systems | Theory and Algorithms

Publication

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, August 4-8

First Page

1518

Last Page

1527

ISBN

9781450362016

Identifier

10.1145/3292500.3330939

Publisher

ACM

City or Country

New York

Embargo Period

5-20-2025

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3292500.3330939

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