Enhancing Cross Domain Recommendation with Domain Dependent Tags

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016, pp. 1266 - 1273
Issue Date:
2016-10-10
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One challenge in recommender system is to deal with data sparsity. To handle this issue, social tags are utilized to bring disjoint domains together for knowledge transfer in cross-domain recommendation. The most intuitive way is to use common tags that present in both source and target domains. However, it is difficult to obtain a strong domain connection by exploiting a small amount of common tags, especially when the tagging data in target domain is too scarce to share enough common tags with source domain. In this paper we propose a novel framework, called Enhanced Tag-induced Cross Domain Collaborative Filtering (ETagiCDCF), to integrate the rich information contained in domain dependent tags into recommendation procedure. We perform experiments on two public datasets and compare with several single and cross domain recommendation approaches, the results demonstrate that ETagiCDCF can effectively address data sparseness and improve recommendation performance.
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