A graph-based recommendation across heterogeneous domains

Deqing Yang, Jingrui He, Huazheng Qin, Yanghua Xiao, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.

Original languageEnglish (US)
Title of host publicationCIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages463-472
Number of pages10
ISBN (Electronic)9781450337946
DOIs
StatePublished - Oct 17 2015
Externally publishedYes
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume19-23-Oct-2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Keywords

  • Cross-domain recommendation
  • Graph propagation
  • Heterogenous domains
  • Semantic matching

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Yang, D., He, J., Qin, H., Xiao, Y., & Wang, W. (2015). A graph-based recommendation across heterogeneous domains. In CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management (pp. 463-472). (International Conference on Information and Knowledge Management, Proceedings; Vol. 19-23-Oct-2015). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806523