research-article

Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation

Authors:

Tommy W. S. ChowAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 823 - 832

https://doi.org/10.1145/3543507.3583240

Published: 30 April 2023 Publication History

Abstract

Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95% parameter storage savings.

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Cited By

Malik NSangwan NBohra NKumari ASheoran DDabas M(2024)HyperSegRec: enhanced hypergraph-based recommendation system with user segmentation and item similarity learningCluster Computing10.1007/s10586-024-04560-x27:8(11727-11745)Online publication date: 3-Jun-2024
https://doi.org/10.1007/s10586-024-04560-x
Wei TMa JChow TChen HDuh WHuang HKato MMothe JPoblete B(2023)Collaborative Residual Metric LearningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591649(1107-1116)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591649

Index Terms

Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

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Cited By

Malik NSangwan NBohra NKumari ASheoran DDabas M(2024)HyperSegRec: enhanced hypergraph-based recommendation system with user segmentation and item similarity learningCluster Computing10.1007/s10586-024-04560-x27:8(11727-11745)Online publication date: 3-Jun-2024
https://doi.org/10.1007/s10586-024-04560-x
Wei TMa JChow TChen HDuh WHuang HKato MMothe JPoblete B(2023)Collaborative Residual Metric LearningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591649(1107-1116)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591649

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