Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks
AbstractPreparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations.
|Publication size in sheets||0.5|
|Book||Sen Shilad, Geyer Werner, Freyne Jill, Castells Pablo (eds.): Proceedings of the 10th ACM Conference on Recommender Systems, 2016, ACM, ISBN 978-1-4503-4035-9, 468 p.|
|Keywords in English||recommender system; matrix factorization; recurrent neural network; session-aware recommendations|
|Score|| = 15.0, 27-03-2017, BookChapterMatConf|
= 15.0, 27-03-2017, BookChapterMatConf
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