Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering

Oren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفRecSys 2020 - 14th ACM Conference on Recommender Systems
ناشرAssociation for Computing Machinery, Inc
عدد الصفحات6
رقم المعيار الدولي للكتب (الإلكتروني)9781450375832
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 22 سبتمبر 2020
منشور خارجيًانعم
الحدث14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, البرازيل
المدة: ٢٢ سبتمبر ٢٠٢٠٢٦ سبتمبر ٢٠٢٠

سلسلة المنشورات

الاسمRecSys 2020 - 14th ACM Conference on Recommender Systems


!!Conference14th ACM Conference on Recommender Systems, RecSys 2020
المدينةVirtual, Online

ملاحظة ببليوغرافية

Publisher Copyright:
© 2020 ACM.


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