research-article

Block-Aware Item Similarity Models for Top-N Recommendation

Authors:

Maarten De RijkeAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 4

Article No.: 42, Pages 1 - 26

https://doi.org/10.1145/3411754

Published: 10 September 2020 Publication History

Abstract

Top-N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods; (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency; and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top-N recommendation.

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Index Terms

Block-Aware Item Similarity Models for Top-N Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 4

October 2020

375 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3402434

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 10 September 2020

Accepted: 01 July 2020

Revised: 01 May 2020

Received: 01 December 2019

Published in TOIS Volume 38, Issue 4

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The Key Research and Technology Development Projects of Anhui Province
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Alkoshman MAtiewi S(2023)Empirical Comparison of Energy Efficiency Between 3-Tier and High-Speed 3-Tier Network TopologiesInternational Journal of Cloud Applications and Computing10.4018/IJCAC.33276613:1(1-28)Online publication date: 14-Nov-2023
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https://dl.acm.org/doi/10.1145/3611385
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