Research Article
A Cross Domain Collaborative Filtering Algorithm Based on Latent Factor Alignment and Two-Stage Matrix Adjustment
@INPROCEEDINGS{10.1007/978-3-319-73317-3_54, author={Xu Yu and Junyu Lin and Feng Jiang and Yan Chu and Jizhong Han}, title={A Cross Domain Collaborative Filtering Algorithm Based on Latent Factor Alignment and Two-Stage Matrix Adjustment}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Cross Domain Collaborative Filtering Knowledge transfer Latent Factor Alignment Constrained UV decomposition model}, doi={10.1007/978-3-319-73317-3_54} }- Xu Yu
Junyu Lin
Feng Jiang
Yan Chu
Jizhong Han
Year: 2018
A Cross Domain Collaborative Filtering Algorithm Based on Latent Factor Alignment and Two-Stage Matrix Adjustment
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_54
Abstract
Sparsity is a tough problem in a single domain Collaborative Filtering (CF) recommender system. In this paper, we propose a cross domain collaborative filtering algorithm based on Latent Factor Alignment and Two-Stage Matrix Adjustment (LFATSMA) to alleviate this difficulty. Unlike previous Cross Domain Collaborative Filtering (CDCF) algorithms, we first align the latent factors across different domains by pattern matching technology. Then we smooth the user and item latent vectors in the target domain by transferring the preferences of similar users and the contents of similar items from the auxiliary domain, which can effectively weaken the effect of noise. Finally, we convert the traditional UV decomposition model to a constrained UV decomposition model, which can effectively keep the balance between under-fitting and over-fitting. We conduct extensive experiments to show that the proposed LFATSMA algorithm performs better than many state-of-the-art CF methods.
