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.
|Title of host publication||CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|Publication status||Published - 2021 Oct 26|
|Event||30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia|
Duration: 2021 Nov 1 → 2021 Nov 5
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||30th ACM International Conference on Information and Knowledge Management, CIKM 2021|
|Period||21/11/1 → 21/11/5|
Bibliographical notePublisher Copyright:
© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)