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2017 | OriginalPaper | Chapter

Learning Intermediary Category Labels for Personal Recommendation

Authors : Wenli Yu, Li Li, Jingyuan Wang, Dengbao Wang, Yong Wang, Zhanbo Yang, Min Huang

Published in: Web and Big Data

Publisher: Springer International Publishing

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Abstract

In many recommender systems, category information has been used as additional features for recommender for quite some time, whose application has tended to be understand relationships between products in order to surface recommendations that are relevant to a given context. Nevertheless, the categories as intermediary are labels for not only attributes of products but also preference characteristics of people, is ignored. Here we propose a framework to learn the intermediary role of categories acting as a bridge between users and items. The framework includes two parts. Firstly, we collect the intermediary factors that category labels affect attributes of items and user preferences respectively. Secondly, we integrate the category medium of assemble item attributes and user preferences to online recommender systems to help users discover similar or complementary products. We evaluate our framework on the Amazon product catalog and demonstrate hierarchy categories can capture characteristics of users and items simultaneously.

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Metadata
Title
Learning Intermediary Category Labels for Personal Recommendation
Authors
Wenli Yu
Li Li
Jingyuan Wang
Dengbao Wang
Yong Wang
Zhanbo Yang
Min Huang
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-63564-4_10

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