Sequential User-based Recurrent Neural Network Recommendations
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Open Access
- 27 August 2017
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 152-160
Abstract
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.Keywords
This publication has 28 references indexed in Scilit:
- Collaborative Deep Learning for Recommender Systems2015
- An overview on the exploitation of time in collaborative filteringWIREs Data Mining and Knowledge Discovery, 2015
- Deep learning in neural networks: An overviewNeural Networks, 2015
- Automated Generation of Music Playlists: Survey and ExperimentsACM Computing Surveys, 2014
- Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocolsUser Modeling and User-Adapted Interaction, 2013
- Hybreed: A software framework for developing context-aware hybrid recommender systemsUser Modeling and User-Adapted Interaction, 2012
- Adaptive least squares support vector machines filter for hand tremor canceling in microsurgeryInternational Journal of Machine Learning and Cybernetics, 2011
- Collaborative filtering with temporal dynamicsCommunications of the ACM, 2010
- Long Short-Term MemoryNeural Computation, 1997
- Learning Complex, Extended Sequences Using the Principle of History CompressionNeural Computation, 1992