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Side Information Fusion for Recommender Systems over Heterogeneous Information Network

Authors:

Dik Lun LeeAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 4

Article No.: 60, Pages 1 - 32

https://doi.org/10.1145/3441446

Published: 10 June 2021 Publication History

Abstract

Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.

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Jomsri PPrangchumpol DPoonsilp KPanityakul T(2024)Hybrid recommender system model for digital library from multiple online publishersF1000Research10.12688/f1000research.133013.312(1140)Online publication date: 18-Nov-2024
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Index Terms

Side Information Fusion for Recommender Systems over Heterogeneous Information Network
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering
      2. Data cleaning

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 4

August 2021

486 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3458847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 ACM.

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Publication History

Published: 10 June 2021

Accepted: 01 December 2020

Revised: 01 August 2020

Received: 01 September 2019

Published in TKDD Volume 15, Issue 4

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https://doi.org/10.12688/f1000research.133013.3
Jomsri PPrangchumpol DPoonsilp KPanityakul T(2024)Hybrid recommender system model for digital library from multiple online publishersF1000Research10.12688/f1000research.133013.212(1140)Online publication date: 4-Apr-2024
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Ntini SSibanda ETemidayo O(2024)Multidimensional Insights into Recommender Systems: A Systematic Review of Evaluation Metrics and Thematic ApplicationsSoftware Engineering Methods Design and Application10.1007/978-3-031-70285-3_29(382-403)Online publication date: 23-Oct-2024
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