Sequential User-based Recurrent Neural Network Recommendations
Paolo Cremonesi;Francesco Ricci;Shlomo Berkovsky;Alexander Tuzhilin (Hrsg). Proceedings of the 11th ACM Conference on Recommender Systems RecSys 2017, August 27-31, 2017, Como, Italy. New York, NY: ACM Association for Computing Machinery 2017 S. 152 - 160
Erscheinungsjahr: 2017
ISBN/ISSN: 978-1-4503-4652-8
Publikationstyp: Diverses (Konferenzbeitrag)
Sprache: Englisch
Doi/URN: 10.1145/3109859.3109877
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Inhaltszusammenfassung
Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We sho...Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.» weiterlesen» einklappen