Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
This addresses the problem of improving recommendation explainability and user modeling for users and developers in recommender systems, with incremental advancements in persona-based methods.
The paper tackled the challenges of modeling users with heterogeneous taste and providing explainable recommendations by proposing the AMP-CF model, which breaks users into latent personas for dynamic representation and explanation, showing competitive performance with state-of-the-art models on five datasets.
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.