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DeepStyle: Learning User Preferences for Visual Recommendation

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Published:07 August 2017Publication History

ABSTRACT

Visual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model items in a common visual feature space. In these methods, visual representations always can only capture the categorical information but fail in capturing the styles of items. Style information indicates the preferences of users and has significant effect in visual recommendation. Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users. Experiments conducted on two real-world datasets illustrate the effectiveness of DeepStyle for visual recommendation.

References

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      • Published in

        cover image ACM Conferences
        SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
        August 2017
        1476 pages
        ISBN:9781450350228
        DOI:10.1145/3077136

        Copyright © 2017 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 August 2017

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        • short-paper

        Acceptance Rates

        SIGIR '17 Paper Acceptance Rate78of362submissions,22%Overall Acceptance Rate792of3,983submissions,20%

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