Improving Incremental Recommenders with Online Bagging

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Vinagre, J (Author) Other; Jorge, AM (Author) FCUP; João Gama (Author) FEP

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Pages: 597-607

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

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Other information

Authenticus ID: P-00M-YEW

DOI: 10.1007/978-3-319-65340-2_49

Abstract (EN): Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positiveonly user feedback, often known as binary ratings. Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11