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)
Number of pages8
ISBN (Print)978-1-4503-4035-9
Publication statusPublished - 1 Sep 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States
Duration: 15 Sep 201619 Sep 2016


Conference10th ACM Conference on Recommender Systems, RecSys 2016
CountryUnited States
CityBoston, MA
Internet address

ID: 11401496