User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning

Abstract

Cross-domain recommendation (CDR) is an effective approach to boost user experience and expand business. Traditional CDR methods generally rely on sharing user-relevant data between domains (e.g., user-item interaction data or user-overlap information). However, this approach is unrealistic in many practical applications, due to user data policies. Some works attempt to circumvent this limitation by leveraging other forms of overlapped data (e.g., item-, content-, or tag-overlap). However, such a solution is not always possible, especially if these forms of overlap are either non-existent or unknown. Until now, there have been limited studies focusing on the intractable CDR task without relying on sharing user-relevant data or using other types of overlap information.