research-article

Context-Adaptive Matrix Factorization for Multi-Context Recommendation

Authors:

Xueqi ChengAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 901 - 910

https://doi.org/10.1145/2806416.2806503

Published: 17 October 2015 Publication History

Abstract

Data sparsity is a long-standing challenge for recommender systems based on collaborative filtering. A promising solution for this problem is multi-context recommendation, i.e., leveraging users' explicit or implicit feedback from multiple contexts. In multi-context recommendation, various types of interactions between entities (users and items) are combined to alleviate data sparsity of a single context in a collective manner. Two issues are crucial for multi-context recommendation: (1) How to differentiate context-specific factors from entity-intrinsic factors shared across contexts? (2) How to capture the salient phenomenon that some entities are insensitive to contexts while others are remarkably context-dependent? Previous methods either do not consider context-specific factors, or assume that a context imposes equal influence on different entities, limiting their capability of combating data sparsity problem by taking full advantage of multiple contexts.

In this paper, we propose a context-adaptive matrix factorization method for multi-context recommendation by simultaneously modeling context-specific factors and entity-intrinsic factors in a unified model. We learn an entity-intrinsic latent factor for every entity, and a context-specific latent factor for every entity in each context. Meanwhile, using a context-entity mixture parameter matrix we explicitly model the extent to which each context imposes influence on each entity. Experiments on two real scenarios demonstrate that our method consistently outperforms previous multi-context recommendation methods on all different sparsity levels.Such a consistent performance promotion forms the unique superiority of our method, enabling it to be a reliable model for multi-context recommendation.

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Cited By

Wang HYang WMan DWang WLv JEr M(2023)A novel cross-network node pair embedding methodology for anchor link predictionWorld Wide Web10.1007/s11280-023-01154-226:5(2495-2520)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s11280-023-01154-2
Wang HYang WWang WMan DLv J(2022)A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial AttacksACM Transactions on Privacy and Security10.1145/354868526:1(1-32)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548685
STITINI OKALOUN SBENCHAREF O(2022)Integrating contextual information into multi-class classification to improve the context-aware recommendationProcedia Computer Science10.1016/j.procs.2021.12.246198:C(311-316)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.procs.2021.12.246
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Index Terms

Context-Adaptive Matrix Factorization for Multi-Context Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

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Published: 17 October 2015

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Wang HYang WMan DWang WLv JEr M(2023)A novel cross-network node pair embedding methodology for anchor link predictionWorld Wide Web10.1007/s11280-023-01154-226:5(2495-2520)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s11280-023-01154-2
Wang HYang WWang WMan DLv J(2022)A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial AttacksACM Transactions on Privacy and Security10.1145/354868526:1(1-32)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548685
STITINI OKALOUN SBENCHAREF O(2022)Integrating contextual information into multi-class classification to improve the context-aware recommendationProcedia Computer Science10.1016/j.procs.2021.12.246198:C(311-316)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.procs.2021.12.246
Xiao YHu RLi DWu JZhen YRen L(2021)Multi-level Graph Attention Network based Unsupervised Network Alignment2021 IEEE 46th Conference on Local Computer Networks (LCN)10.1109/LCN52139.2021.9524999(217-224)Online publication date: 4-Oct-2021
https://doi.org/10.1109/LCN52139.2021.9524999
Chen HYIN HSun XChen TGabrys BMusial KGupta RLiu YShah MRajan STang JPrakash B(2020)Multi-level Graph Convolutional Networks for Cross-platform Anchor Link PredictionProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403201(1503-1511)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403201
Liu LLi XCheung WLiao L(2020)Structural Representation Learning for User Alignment Across Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291151632:9(1824-1837)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1109/TKDE.2019.2911516
Idrissi NZellou A(2020)A systematic literature review of sparsity issues in recommender systemsSocial Network Analysis and Mining10.1007/s13278-020-0626-210:1Online publication date: 11-Feb-2020
https://doi.org/10.1007/s13278-020-0626-2
Imran MYin HChen TShao YZhang XZhou X(2020)Decentralized Embedding Framework for Large-Scale NetworksDatabase Systems for Advanced Applications10.1007/978-3-030-59419-0_26(425-441)Online publication date: 22-Sep-2020
https://doi.org/10.1007/978-3-030-59419-0_26
Li MWu HZhang H(2019)Matrix Factorization for Personalized Recommendation With Implicit Feedback and Temporal Information in Social Ecommerce NetworksIEEE Access10.1109/ACCESS.2019.29439597(141268-141276)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2943959
Raza SDing C(2019)Progress in context-aware recommender systems — An overviewComputer Science Review10.1016/j.cosrev.2019.01.00131:C(84-97)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1016/j.cosrev.2019.01.001
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