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Recommendation in heterogeneous information networks with implicit user feedback

Authors:

Xiao Yu,

Jiawei HanAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 347 - 350

https://doi.org/10.1145/2507157.2507230

Published: 12 October 2013 Publication History

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Abstract

Recent studies suggest that by using additional user or item relationship information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous information network environment. In this paper, we study the entity recommendation problem in heterogeneous information networks. We propose to combine various relationship information from the network with user feedback to provide high quality recommendation results.

The major challenge of building recommender systems in heterogeneous information networks is to systematically define features to represent the different types of relationships between entities, and learn the importance of each relationship type. In the proposed framework, we first use meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network. We then define a recommendation model with such latent features and use Bayesian ranking optimization techniques to estimate the model. Empirical studies show that our approach outperforms several widely employed implicit feedback entity recommendation techniques.

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Cited By

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Zhang HWang DSun ZLi YSun YLiang HWang W(2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
https://doi.org/10.1145/3713071
Li HZheng JJin BZhu H(2024)Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender SystemsElectronics10.3390/electronics1318359413:18(3594)Online publication date: 10-Sep-2024
https://doi.org/10.3390/electronics13183594
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
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Index Terms

Recommendation in heterogeneous information networks with implicit user feedback
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

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Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Zhang HWang DSun ZLi YSun YLiang HWang W(2025)KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?ACM Transactions on Information Systems10.1145/3713071Online publication date: 21-Jan-2025
https://doi.org/10.1145/3713071
Li HZheng JJin BZhu H(2024)Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender SystemsElectronics10.3390/electronics1318359413:18(3594)Online publication date: 10-Sep-2024
https://doi.org/10.3390/electronics13183594
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Zhou YFang YMa CHou THuang X(2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
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Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
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Liu JShang LSu YNie WWen XLiu A(2024)Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge GraphACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363970620:5(1-18)Online publication date: 5-Jan-2024
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Zhang HYang XBai LLiang J(2024)Enhancing Drug Recommendations via Heterogeneous Graph Representation Learning in EHR NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3329025(1-12)Online publication date: 2024
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Recommendations

Personalized entity recommendation: a heterogeneous information network approach

A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups

Recommendation for Repeat Consumption from User Implicit Feedback

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