Online Gradient Boosting for Incremental Recommender Systems

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

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

Conference proceedings International

Title: Discovery Science - 21st International Conference, DS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings

Pages: 209-223

21st International Conference on Discovery Science, DS 2018

29 October 2018 through 31 October 2018

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: P-00P-PAS

DOI: 10.1007/978-3-030-01771-2_14

Abstract (EN): Ensemble models have been proven successful for batch recommendation algorithms, however they have not been well studied in streaming applications. Such applications typically use incremental learning, to which standard ensemble techniques are not trivially applicable. In this paper, we study the application of three variants of online gradient boosting to top-N recommendation tasks with implicit data, in a streaming data environment. Weak models are built using a simple incremental matrix factorization algorithm for implicit feedback. Our results show a significant improvement of up to 40% over the baseline standalone model. We also show that the overhead of running multiple weak models is easily manageable in stream-based applications. © 2018, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific