Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering

Oren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes"in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.

Original languageEnglish
Title of host publicationRecSys 2020 - 14th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages468-473
Number of pages6
ISBN (Electronic)9781450375832
DOIs
StatePublished - 22 Sep 2020
Event14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, Brazil
Duration: 22 Sep 202026 Sep 2020

Publication series

NameRecSys 2020 - 14th ACM Conference on Recommender Systems

Conference

Conference14th ACM Conference on Recommender Systems, RecSys 2020
Country/TerritoryBrazil
CityVirtual, Online
Period22/09/2026/09/20

Keywords

  • Attention Models
  • Recommender Systems
  • Representation Learning

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