research-article

Enhancing matrix factorization through initialization for implicit feedback databases

Authors:

Balázs Hidasi,

Domonkos TikkAuthors Info & Claims

CaRR '12: Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation

Pages 2 - 9

https://doi.org/10.1145/2162102.2162104

Published: 14 February 2012 Publication History

Abstract

The implicit feedback based recommendation problem---when only the user history is available but there are no ratings---is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Still, this practically important recommendation task received less attention and therefore there are only a few common implicit feedback based algorithms and benchmark datasets. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors, where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. The efficiency of the initialization is evaluated using various context and metadata based similarity concepts on two implicit variants of the MovieLens 10M dataset and one real life implicit database. It is shown that performance gain can attain 10% improvement in recall@50 and in AUC@50.

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Cited By

Costa JMarinho LSantos RParra D(2023)Evaluating Pre-training Strategies for Collaborative FilteringProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592949(175-182)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592949
Marinho Lda Costa JParra DSantos R(2022)Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving EmbeddingsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511104(782-793)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511104
Huang TZhao RBi LZhang DLu C(2022)Neural Embedding Singular Value Decomposition for Collaborative FilteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.307085333:10(6021-6029)Online publication date: Oct-2022
https://doi.org/10.1109/TNNLS.2021.3070853
Show More Cited By

Index Terms

Enhancing matrix factorization through initialization for implicit feedback databases
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Markov decision processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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cover image ACM Other conferences

CaRR '12: Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation

February 2012

41 pages

ISBN:9781450311922

DOI:10.1145/2162102

Conference Chairs:
Ernesto W. De Luca
University of Applied Sciences Potsdam
,
Matthias Böhmer
DFKI GmbH
,
Alan Said
Technische Universität Berlin
,
Ed H. Chi
Google Inc.

Copyright © 2012 ACM.

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Published: 14 February 2012

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CaRR '12: 2nd Workshop on Context-awareness in Retrieval and Recommendation

February 14, 2012

Lisbon, Portugal

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Cited By

Costa JMarinho LSantos RParra D(2023)Evaluating Pre-training Strategies for Collaborative FilteringProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592949(175-182)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592949
Marinho Lda Costa JParra DSantos R(2022)Similarity-Based Explanations meet Matrix Factorization via Structure-Preserving EmbeddingsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511104(782-793)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511104
Huang TZhao RBi LZhang DLu C(2022)Neural Embedding Singular Value Decomposition for Collaborative FilteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.307085333:10(6021-6029)Online publication date: Oct-2022
https://doi.org/10.1109/TNNLS.2021.3070853
Peña FO'Reilly-Morgan DTragos EHurley NDuriakova ESmyth BLawlor A(2020)Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N RecommendationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412207(438-443)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412207
Lavee GKoenigstein NBarkan OBogers TSaid ABrusilovsky PTikk D(2019)When actions speak louder than clicksProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347044(287-295)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347044
Deldjoo YSchedl MHidasi BKnees PPera SEkstrand MAmatriain XO'Donovan J(2018)Multimedia recommender systemsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241620(537-538)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241620

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