research-article

Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

Authors:

Laurent Charlin,

David M. BleiAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 59 - 66

https://doi.org/10.1145/2959100.2959182

Published: 07 September 2016 Publication History

Abstract

Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, CoFactor, which jointly decomposes the user-item interaction matrix and the item-item co-occurrence matrix with shared item latent factors. For each pair of items, the co-occurrence matrix encodes the number of users that have consumed both items. CoFactor is inspired by the recent success of word embedding models (e.g., word2vec) which can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the performance over MF models on several datasets with little additional computational overhead. We provide qualitative results that explain how CoFactor improves the quality of the inferred factors and characterize the circumstances where it provides the most significant improvements.

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Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

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cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 ACM.

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Published: 07 September 2016

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RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

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

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Cited By

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https://doi.org/10.3390/electronics13142811
Peng SSiet SIlkhomjon SKim DPark D(2024)Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation SystemsApplied Sciences10.3390/app1403115514:3(1155)Online publication date: 30-Jan-2024
https://doi.org/10.3390/app14031155
Chen YLee W(2024)An Evolving Preference-Based Recommendation SystemIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33439988:2(1118-1124)Online publication date: Apr-2024
https://doi.org/10.1109/TETCI.2023.3343998
Khalilzadeh FCicekli I(2024)REHREC: Review Effected Heterogeneous Information Network Recommendation SystemIEEE Access10.1109/ACCESS.2024.337927112(42751-42760)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3379271
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Eberhard LPopova KWalk SHelic D(2024)Computing recommendations from free-form textExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121268236:COnline publication date: 1-Feb-2024
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Yu TZhang LLiu HLiu HWang J(2024)Service recommendation based on contrastive learning and multi-task learningComputer Communications10.1016/j.comcom.2023.11.018213(285-295)Online publication date: Jan-2024
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Bevec MTkalčič MPesek M(2024)Hybrid music recommendation with graph neural networksUser Modeling and User-Adapted Interaction10.1007/s11257-024-09410-4Online publication date: 24-Aug-2024
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Zhang XLiu WZhou W(2024)Conversational Recommendation Based on Graph Neural Network Model with Dual Attention MechanismCommunications, Signal Processing, and Systems10.1007/978-981-99-7502-0_29(265-273)Online publication date: 18-Apr-2024
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Samih AHamane ZGhadi AFennan A(2024)Health Recommender SystemsInternational Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023)10.1007/978-3-031-52388-5_25(261-272)Online publication date: 9-Feb-2024
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