Learning hierarchical feature influence for recommendation by recursive regularization

Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang

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

14 Citations (Scopus)

Abstract

Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.

Original languageEnglish
Title of host publicationRecSys 2016
Subtitle of host publicationProceedings of the 10th ACM Conference on Recommender Systems
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages51-58
Number of pages8
ISBN (Electronic)9781450340359
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: 15 Sep 201619 Sep 2016

Conference

Conference10th ACM Conference on Recommender Systems, RecSys 2016
CountryUnited States
CityBoston
Period15/09/1619/09/16

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  • Cite this

    Yang, J., Sun, Z., Bozzon, A., & Zhang, J. (2016). Learning hierarchical feature influence for recommendation by recursive regularization. In RecSys 2016: Proceedings of the 10th ACM Conference on Recommender Systems (pp. 51-58). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/2959100.2959159