Canonical Correlation Analysis in Cross-Domain Recommendation

Cross-domain recommendation has recently emerged as a hot topic in the field of recommender systems. The idea is to use rating information accumulated in one domain (known as a source or auxiliary domain) to improve the quality of recommendations in another domain (known as a target domain). One of the important problems in cross-domain recommendation is the selection of source domains appropriate for a target domain. Previous works mostly assume that the best domain pairs can be decided based on similarity of their nature (such as books and movies), or simulate domain pairs by splitting the same dataset into multiple domains.\ud We argue that the success of cross-domain recommendations depends on domain characteristics and shared (latent) information among domains; therefore posing new questions: What makes a good auxiliary domain? How should we choose the best auxiliary domain for a specific target domain?\ud In this dissertation we examine the success and failure of cross-domain collaborative filtering across three different datasets with various characteristics of domains. Our goals are to explore the added value of cross-domain recommendations in comparison with traditional within-domain recommendations, and to achieve some progress in uncovering the main mystery of cross-domain recommendation: how can we determine whether a pair of domains is a good candidate for applying cross-domain recommendation techniques? For the former goal, we propose a cross-domain collaborative filtering approach based on canonical correlation analysis. In order to address the latter goal, we investigate a canonical correlation approach as a possible predictor of successful domain pairs and examine a range of features of a single domain and domain pairs in order to see how they could be used to improve predictions. Eventually, we propose a domain-pair classifier that can distinguish between the beneficial and non-beneficial domain pairs before performing the recommendations