Abstract
For cross-domain recommendation, it can be divided into strong correlation and weak correlation problems according to the consistency between auxiliary domain and target domain. The weak correlation problem is more practical than the strong correlation problem, and the solution is more difficult. The difficulty lies in how to establish an effective transfer model, to make sure the auxiliary domain and the target domain can perform effective collaborative training. For weak correlation problem, if the item-side of auxiliary domain and the target domain are not aligned, or the transfer model has a strong dependency on the user-side of the auxiliary domain, it will seriously affect the effect of cross-domain recommendation. To solve these problems mentioned above, we propose a CCA-based item-side alignment method (CIAM) by introducing: (1) item side alignment method. We use CCA to align the item side between auxiliary and target domain, to intensify the weak correlation between 2 domains. (2) the transfer model of retaining the user feature of target domain. The CIAM retained user features of target domain in UV decomposition, that makes the transfer model could not destroy the user feature between 2 domains. The proposed CIAM can improve the assistance of auxiliary domain, and can avoid the influence of the needless user-side of the auxiliary do-main on cross-domain recommendation. By experimental analysis, it can be verified that the proposed CIAM algorithm has a better performance than general cross-domain recommendation methods.
Highlights
With the development of social information industry, the volume of movies, music, online shopping and other industries continues to grow
The latent factor models (LFM) are based on the factorization method to make cross domain recommendation. These methods establish the association between auxiliary domain and target domain by extracting and transferring latent factor obtained by matrix decomposition
In this paper, a cross-domain recommendation algorithm CCA-based item-side alignment method (CIAM) based on item-side alignment is proposed
Summary
With the development of social information industry, the volume of movies, music, online shopping and other industries continues to grow. Xin: CCA-Based Item-Side Alignment Method for Cross-Domain Recommendation System. If synthesize the rating data in multiple domains (such as music, comics, games), and perform analysis in a certain single domain (such as movies), it can improve the recommendation accuracy in the single domain By this principle, many cross-domain recommendation methods are born, and gradually become the research focus of recommendation system [4]. The objective of transfer learning model is to express the commonality, correlation and association among multiple domains. The adopted CCA can ensure that the aligned item has the greatest commonality on transfer learning, to improve the rationality of cross-domain recommendations. The proposed CIAM only transfer the aligned item features from auxiliary domain to target domain.
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