Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering

TL;DR

This paper introduces AMP-CF, a neural model that captures user heterogeneity through multiple personas, providing both personalized recommendations and explanations, and demonstrates its effectiveness across various domains.

Contribution

The paper presents a novel attentive multi-persona collaborative filtering model that models users as a mixture of personas for improved explainability and recommendation accuracy.

Findings

AMP-CF achieves competitive performance on five datasets.

The model effectively explains recommendations through user personas.

A new evaluation scheme assesses item relevance based on user taste distribution.

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.