GLocal-K: Global and Local Kernels for Recommender Systems

Soyeon Caren Han, Taejun Lim, Siqu Long, Bernd Burgstaller, Josiah Poon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a G lobal-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.

Original languageEnglish
Title of host publicationCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages3063-3067
Number of pages5
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - 2021 Oct 26
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: 2021 Nov 12021 Nov 5

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Country/TerritoryAustralia
CityVirtual, Online
Period21/11/121/11/5

Bibliographical note

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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