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short-paper

Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering

Authors:

Noam KoenigsteinAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 468 - 473

https://doi.org/10.1145/3383313.3412226

Published: 22 September 2020 Publication History

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.

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Cited By

Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Elisha YBarkan OKoenigstein NSerra ESpezzano F(2024)Probabilistic Path Integration with Mixture of Baseline DistributionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679641(570-580)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679641
Barkan OToib YElisha YKoenigstein NSerra ESpezzano F(2024)A Learning-based Approach for Explaining Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679548(98-108)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679548
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Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 September 2020

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Israel Science Foundation

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RecSys '20

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/36528633:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3652863
Elisha YBarkan OKoenigstein NSerra ESpezzano F(2024)Probabilistic Path Integration with Mixture of Baseline DistributionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679641(570-580)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679641
Barkan OToib YElisha YKoenigstein NSerra ESpezzano F(2024)A Learning-based Approach for Explaining Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679548(98-108)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679548
Barkan OBogina VGurevitch LAsher YKoenigstein NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Counterfactual Framework for Learning and Evaluating Explanations for Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645560(3723-3733)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645560
Barkan OElisha YWeill JAsher YEshel AKoenigstein NFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Deep Integrated ExplanationsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614836(57-67)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614836
Barkan OAsher YEshel AElisha YKoenigstein N(2023)Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00105(944-949)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00105
Barkan OElisha YWeill JAsher YEshel AKoenigstein N(2023)Stochastic Integrated Explanations for Vision Models2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00104(938-943)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00104
Barkan OElisha YAsher YEshel AKoenigstein N(2023)Visual Explanations via Iterated Integrated Attributions2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00198(2073-2084)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00198
Barkan OShaked TFuchs YKoenigstein N(2023)Modeling users’ heterogeneous taste with diversified attentive user profilesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09376-934:2(375-405)Online publication date: 1-Aug-2023
https://doi.org/10.1007/s11257-023-09376-9
Zhang SZhang AYao L(2023)Recommender SystemsMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_28(637-658)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_28
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