Techniques for cross-domain recommendation:A survey

doi:10.3969/j.issn.1000-5641.2017.05.010

Abstract

Abstract: With the rapid development of information technology and Internet, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Personalized recommendation system is a popular technology to deal with the information overload and recommendation algorithms are the core of it. In the past decades, collaborative filtering recommendation algorithm based on single domain has been widely used in many applications. However, the problems of cold start and data sparsity usually result in overfitting and fail to give desirable performance. The cross-domain recommendation techniques have been a hot topic in the field of recommender systems, which aim to utilize knowledge from related domains to perform or improve recommendation in the target domain. This paper carries out a systematic study and analysis of cross-domain recommendation techniques. First, we summarize the related concepts and the technical difficulties of cross-domain recommendation algorithms. Second, we present a general categorization of cross-domain recommendation techniques and sum up their respective advantages and disadvantages. Finally we introduce the method of performance analysis of cross-domain recommendation algorithm in detail.

Key words: information overload, personalization, cross-domain recommendation algorithms

CLC Number:

TP181

CHEN Lei-hui, KUANG Jun, CHEN Hui, ZENG Wei, ZHENG Jian-bing, GAO Ming. Techniques for cross-domain recommendation:A survey[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 101-116,137.

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