Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

Abstract

Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users’ choices. Intuitively, items that occur within the same “context” (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item’s context would complement the sparse user-item interactions by extending a user’s preference to other items of similar aspects. To realize this intuition, we develop Collaborative Context Poisson Factorization (C2PF), a new Bayesian latent variable model that seamlessly integrates contextual relationships among items into a personalized recommendation approach. We further derive a scalable variational inference algorithm to fit C2PF to preference data. Empirical results on real-world datasets show evident performance improvements over strong factorization models.

Keywords

Machine Learning, Learning Preferences or Rankings, Recommender Systems

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18),Stockholm, Sweden, 2018 July 13-19

First Page

2667

Last Page

2674

ISBN

9780999241127

Identifier

10.24963/ijcai.2018/370

Publisher

IJCAI

City or Country

Vienna

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2018/370

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