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Online Gradient Boosting for Incremental Recommender Systems

Title
Online Gradient Boosting for Incremental Recommender Systems
Type
Article in International Conference Proceedings Book
Year
2018
Authors
Vinagre, J
(Author)
Other
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Jorge, AM
(Author)
FCUP
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João Gama
(Author)
FEP
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Conference proceedings International
Pages: 209-223
21st International Conference on Discovery Science, DS 2018
29 October 2018 through 31 October 2018
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Other information
Authenticus ID: P-00P-PAS
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
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