research-article

MVIN: Learning Multiview Items for Recommendation

Authors:

Lun-Wei KuAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 99 - 108

https://doi.org/10.1145/3397271.3401126

Published: 25 July 2020 Publication History

Abstract

Researchers have begun to utilize heterogeneous knowledge graphs(KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model that provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. Besides, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.

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Shan YYang JGao Y(2024)GL-GNN: Graph learning via the network of graphsKnowledge-Based Systems10.1016/j.knosys.2024.112107299(112107)Online publication date: Sep-2024
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Index Terms

MVIN: Learning Multiview Items for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

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Published: 25 July 2020

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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

He HHe XPeng YShan ZSu X(2024)Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00354(4657-4670)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00354
Tan YLv HZhou ZGuo WXiong BLiu WChen CWang SYang C(2024)Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00108(1310-1323)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00108
Shan YYang JGao Y(2024)GL-GNN: Graph learning via the network of graphsKnowledge-Based Systems10.1016/j.knosys.2024.112107299(112107)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112107
Xia BQin JHan LGao AMa C(2024)Knowledge filter contrastive learning for recommendationKnowledge and Information Systems10.1007/s10115-024-02158-8Online publication date: 16-Jul-2024
https://doi.org/10.1007/s10115-024-02158-8
Wu JXu YZhang BXu ZLi B(2024)Graph-based Dynamic Preference Modeling for Personalized RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2259-4_27(356-368)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2259-4_27
Li SJia YWu YWei NZhang LGuo J(2023)Knowledge-Aware Graph Self-Supervised Learning for RecommendationElectronics10.3390/electronics1223486912:23(4869)Online publication date: 2-Dec-2023
https://doi.org/10.3390/electronics12234869
Jiang LYan GLuo HChang W(2023)Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive LearningElectronics10.3390/electronics1220423812:20(4238)Online publication date: 13-Oct-2023
https://doi.org/10.3390/electronics12204238
Hou RZhang YOu QLi SHe YWang HZhou Z(2023)Recommendation Method of Power Knowledge Retrieval Based on Graph Neural NetworkElectronics10.3390/electronics1218392212:18(3922)Online publication date: 18-Sep-2023
https://doi.org/10.3390/electronics12183922
Ho TLe AVu D(2023)Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual SourcesApplied Sciences10.3390/app1310632413:10(6324)Online publication date: 22-May-2023
https://doi.org/10.3390/app13106324
Ong RQiu WKhong AFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Quad-Tier Entity Fusion Contrastive Representation Learning for Knowledge Aware Recommendation SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615020(1949-1959)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615020
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