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 publicationProceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016
Place of PublicationBoston, MA
PublisherAssociation for Computing Machinery (ACM)
Pages51-58
Number of pages8
ISBN (Print)978-1-4503-4035-9
StatePublished - 1 Sep 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, MA, United States

Conference

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

ID: 11401496