Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Joao Vinagre (Author) Other; Alipio Mario Jorge (Author) FCUP; Joao Gama (Author) FEP

Conference proceedings International

Title: USER MODELING, ADAPTATION, AND PERSONALIZATION, UMAP 2014

Pages: 459-470

22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP)

Aalborg, DENMARK, JUL 07-11, 2014

Indexing

ISI Web of Knowledge - 58 Citations

Scopus - 67 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00A-1W7

DOI: 10.1007/978-3-319-08786-3_41

Abstract (EN): Traditional Collaborative Filtering algorithms for recommendation are designed for stationary data. Likewise, conventional evaluation methodologies are only applicable in offline experiments, where data and models are static. However, in real world systems, user feedback is continuously being generated, at unpredictable rates. One way to deal with this data stream is to perform online model updates as new data points become available. This requires algorithms able to process data at least as fast as it is generated. One other issue is how to evaluate algorithms in such a streaming data environment. In this paper we introduce a simple but fast incremental Matrix Factorization algorithm for positive-only feedback. We also contribute with a prequential evaluation protocol for recommender systems, suitable for streaming data environments. Using this evaluation methodology, we compare our algorithm with other state-of-the-art proposals. Our experiments reveal that despite its simplicity, our algorithm has competitive accuracy, while being significantly faster.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12