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Non-parametric e-mixture of Density Functions Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Sequential Collaborative Ranking Using (No-)Click Implicit Feedback

Authors : Frédéric Guillou, Romaric Gaudel, Philippe Preux

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

We study Recommender Systems in the context where they suggest a list of items to users. Several crucial issues are raised in such a setting: first, identify the relevant items to recommend; second, account for the feedback given by the user after he clicked and rated an item; third, since new feedback arrive into the system at any moment, incorporate such information to improve future recommendations. In this paper, we take these three aspects into consideration and present an approach handling click/no-click feedback information. Experiments on real-world datasets show that our approach outperforms state of the art algorithms.

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previous chapter Automatic Design of Neural Network Structures Using AiS
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Metadata
Title
Sequential Collaborative Ranking Using (No-)Click Implicit Feedback
Authors
Frédéric Guillou
Romaric Gaudel
Philippe Preux
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46671-2

Electronic ISBN: 978-3-319-46672-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46672-9_33

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