Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2021
Abstract
Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns to fit the target observations and align its hidden space with the source latent space jointly. Since we model the latent spaces by the variational posteriors, we operate at this level, and in particular, we investigate two approaches, namely rigid and soft alignments. In the former scenario, the variational model in the target domain is set equal to the source variational model. That is, we only learn a generative model in the target domain. In the soft-alignment scenario, the target VAE has its variational model, but which is encouraged to look like its source counterpart. We analyze the proposed objectives theoretically and conduct extensive experiments to illustrate the benefit of our contribution. Empirical results on six real-world datasets show that the proposed models outperform several comparable cross-domain recommendation models.
Keywords
Collaborative Filtering, Cross-Domain Recommendation, Neural Networks, Variational Autoencoder
Discipline
Databases and Information Systems | Data Science
Research Areas
Data Science and Engineering
Publication
RecSys'21: Proceedings of the 15th ACM Conference on Recommender Systems, September 27 - October 1, Virtual
First Page
176
Last Page
186
ISBN
9781450384582
Identifier
10.1145/3460231.3474265
Publisher
ACM
City or Country
New York
Embargo Period
12-13-2021
Citation
SALAH, Aghiles; TRAN, Thanh-Binh; and LAUW, Hady W..
Towards source-aligned variational models for cross-domain recommendation. (2021). RecSys'21: Proceedings of the 15th ACM Conference on Recommender Systems, September 27 - October 1, Virtual. 176-186.
Available at: https://ink.library.smu.edu.sg/sis_research/6430
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3460231.3474265