TY - GEN
T1 - Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
AU - Barkan, Oren
AU - Fuchs, Yonatan
AU - Caciularu, Avi
AU - Koenigstein, Noam
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/9/22
Y1 - 2020/9/22
N2 - 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.
AB - 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.
KW - Attention Models
KW - Recommender Systems
KW - Representation Learning
UR - http://www.scopus.com/inward/record.url?scp=85092718241&partnerID=8YFLogxK
U2 - 10.1145/3383313.3412226
DO - 10.1145/3383313.3412226
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AN - SCOPUS:85092718241
T3 - RecSys 2020 - 14th ACM Conference on Recommender Systems
SP - 468
EP - 473
BT - RecSys 2020 - 14th ACM Conference on Recommender Systems
T2 - 14th ACM Conference on Recommender Systems, RecSys 2020
Y2 - 22 September 2020 through 26 September 2020
ER -