NDLI: Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

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Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

Content Provider	ACM Digital Library
Author	Smirnova, Elena Vasile, Flavian
Abstract	Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.
Starting Page	2
Ending Page	9
Page Count	8
File Format	PDF
ISBN	9781450353533
DOI	10.1145/3125486.3125488
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Recurrent neural networks Context-aware recommendation User sequence modeling Recommender systems
Content Type	Text
Resource Type	Article

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