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Group latent factor model for recommendation with multiple user behaviors

Published: 03 July 2014 Publication History

Abstract

Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users' multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between different behaviors and ignore the heterogeneity of them, which may make improper information transferred and harm the recommendation results. To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors. Thus, the correlation and heterogeneity of multiple behaviors can be modeled by these shared and specific latent factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better.

References

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A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In WSDM, 2012.
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B. Li, Q. Yang, and X. Xue. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In IJCAI, 2009.
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H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In CIKM, 2008.
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R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.
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A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In KDD, 2008.
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Cited By

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  • (2024)V-GMR: a variational autoencoder-based heterogeneous graph multi-behavior recommendation modelApplied Intelligence10.1007/s10489-024-05360-x54:4(3337-3350)Online publication date: 1-Feb-2024
  • (2021)Design and Implementation of Short Video Recommendation Algorithm Based on Latent Factor Model2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC51340.2021.9421151(169-172)Online publication date: 14-Apr-2021
  • (2021)AskMe: joint individual-level and community-level behavior interaction for question recommendationWorld Wide Web10.1007/s11280-021-00964-6Online publication date: 4-Nov-2021
  • Show More Cited By

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  1. Group latent factor model for recommendation with multiple user behaviors

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      cover image ACM Conferences
      SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
      July 2014
      1330 pages
      ISBN:9781450322577
      DOI:10.1145/2600428
      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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      Publication History

      Published: 03 July 2014

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      Author Tags

      1. matrix factorization
      2. recommender systems

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      SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      Cited By

      View all
      • (2024)V-GMR: a variational autoencoder-based heterogeneous graph multi-behavior recommendation modelApplied Intelligence10.1007/s10489-024-05360-x54:4(3337-3350)Online publication date: 1-Feb-2024
      • (2021)Design and Implementation of Short Video Recommendation Algorithm Based on Latent Factor Model2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC51340.2021.9421151(169-172)Online publication date: 14-Apr-2021
      • (2021)AskMe: joint individual-level and community-level behavior interaction for question recommendationWorld Wide Web10.1007/s11280-021-00964-6Online publication date: 4-Nov-2021
      • (2020)A Survey on Heterogeneous One-class Collaborative FilteringACM Transactions on Information Systems10.1145/340252138:4(1-54)Online publication date: 11-Aug-2020
      • (2020)Improving Implicit Recommender Systems with Auxiliary DataACM Transactions on Information Systems10.1145/337233838:1(1-27)Online publication date: 6-Feb-2020
      • (2020)Group-Based Recurrent Neural Networks for POI RecommendationACM/IMS Transactions on Data Science10.1145/33430371:1(1-18)Online publication date: 12-Mar-2020
      • (2019)Sampler Design for Bayesian Personalized Ranking by Leveraging View DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2931327(1-1)Online publication date: 2019
      • (2019)An Enhanced Group Recommender System by Exploiting Preference RelationIEEE Access10.1109/ACCESS.2019.28977607(24852-24864)Online publication date: 2019
      • (2018)Improving implicit recommender systems with view dataProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304232(3343-3349)Online publication date: 13-Jul-2018
      • (2018)A Novel Group Recommendation Mechanism From the Perspective of Preference DistributionIEEE Access10.1109/ACCESS.2018.27924276(5865-5878)Online publication date: 2018
      • Show More Cited By

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