short-paper

Latent multi-criteria ratings for recommendations

Authors:

Alexander TuzhilinAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 428 - 431

https://doi.org/10.1145/3298689.3347068

Published: 10 September 2019 Publication History

Abstract

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures.

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Cited By

Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Yi KYamagishi RLi JLi TBai ZMa Q(2024)User-experience oriented POI recommendation with pseudo ratingMultimedia Tools and Applications10.1007/s11042-024-19455-7Online publication date: 28-Jun-2024
https://doi.org/10.1007/s11042-024-19455-7
Airen SAgrawal J(2024)Learning Rate Scheduler for Multi-criterion Movie Recommender SystemSmart Trends in Computing and Communications10.1007/978-981-97-1313-4_26(305-317)Online publication date: 2-Jun-2024
https://doi.org/10.1007/978-981-97-1313-4_26
Show More Cited By

Index Terms

Latent multi-criteria ratings for recommendations
1. Information systems
  1. Information retrieval

Recommendations

User preference learning in multi-criteria recommendations using stacked auto encoders
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Enhanced multi-criteria recommender system based on fuzzy Bayesian approach

In the area of recommender systems, collaborative filtering is widely used technique for recommending appropriate items to a user based on the available ratings given by similar users. Most recommender systems (RSs) work only on the single criterion ...
Multi-criteria Recommendations through Preference Learning
CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

In today's internet era, recommender system (RS) addresses information overload problem, which is common in many information driven domains. RS helps users chose a set of appropriate options from a plethora of options. Traditional single rating ...

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cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 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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Association for Computing Machinery

New York, NY, United States

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Published: 10 September 2019

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

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

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Cited By

Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Yi KYamagishi RLi JLi TBai ZMa Q(2024)User-experience oriented POI recommendation with pseudo ratingMultimedia Tools and Applications10.1007/s11042-024-19455-7Online publication date: 28-Jun-2024
https://doi.org/10.1007/s11042-024-19455-7
Airen SAgrawal J(2024)Learning Rate Scheduler for Multi-criterion Movie Recommender SystemSmart Trends in Computing and Communications10.1007/978-981-97-1313-4_26(305-317)Online publication date: 2-Jun-2024
https://doi.org/10.1007/978-981-97-1313-4_26
Bougteb YFrikh BOuhbi BZemmouri E(2024)Attention-Based Recurrent Neural Network for Multicriteria RecommendationsIntelligent Systems and Applications10.1007/978-3-031-47724-9_18(264-274)Online publication date: 19-Apr-2024
https://doi.org/10.1007/978-3-031-47724-9_18
Fan GZhang CChen JLi PLin YLeung V(2023)Improving Rating Prediction in Multi-criteria Recommender Systems via a Collective Factor ModelSSRN Electronic Journal10.2139/ssrn.4618243Online publication date: 2023
https://doi.org/10.2139/ssrn.4618243
Huijben IKool WPaulus Mvan Sloun R(2023)A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.315704245:2(1353-1371)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TPAMI.2022.3157042
Fan GZhang CChen JLi PLi YLeung V(2023)Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor ModelIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3270910(1-11)Online publication date: 2023
https://doi.org/10.1109/TNSE.2023.3270910
Li PQue MTuzhilin A(2023)Dual Contrastive Learning for Efficient Static Feature Representation in Sequential RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3289469(1-13)Online publication date: 2023
https://doi.org/10.1109/TKDE.2023.3289469
Shrivastava RSingh Sisodia DKumar Nagwani N(2023)Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learningExpert Systems with Applications10.1016/j.eswa.2022.119071213(119071)Online publication date: Mar-2023
https://doi.org/10.1016/j.eswa.2022.119071
Li PTuzhilin A(2022)Learning Latent Multi-Criteria Ratings From User Reviews for RecommendationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303062334:8(3854-3866)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TKDE.2020.3030623
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