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Bring User Interest to Related Entity Recommendation

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Graph Structures for Knowledge Representation and Reasoning (GKR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9501))

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Abstract

Most existing approaches to query recommendation focus on query-term or click based analysis over the user session log or click-through data. For entity query, however, finding the relevant queries from these resources is far from trivial. Entity query is a special kind of short queries that commonly appear in image search, video search or object search. Focusing on related entity recommendation, this paper proposes to collect rich related entities of interest from a large number of entity-oriented web pages. During the collection, we maintain a large-scale and general-purpose related entity network (REN), based upon a special co-occurrence relation between the related entity and target entity. Benefiting from the REN, we can easily incorporate various types of related entity into recommendation. Different ranking methods are employed to recommend related and diverse entities of interest. Extensive experiments are conducted to assess the recommendation performance in term of Accuracy and Serendipity. Experimental results show that the REN is a good recommendation resource with high quality of related entities. For recommending related entity, the proposed REN-based method achieves good performance compared with a state-of-the-art relatedness measurement and two famous recommendation systems.

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Notes

  1. 1.

    http://www.movies.com/amazing-spider-man-2/m68476.

  2. 2.

    The co-occurrence relation limits one of the two entities to be in the title while the other appears in the body text of the same article.

  3. 3.

    http://wikipedia-miner.sourceforge.net.

  4. 4.

    This work uses enwiki dump progress on 20130503.

  5. 5.

    Probase data is publicly available at http://probase.msra.cn/dataset.aspx.

  6. 6.

    This work uses enwiki dump progress on 20130503.

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Acknowledgments

This work was partially supported by the National Key Basic Research Program (973 Program) of China under grant No. 2014CB340403 and the Fundamental Research Funds for the Central Universities & the Research Funds of Renmin University of China. This work was also supported in part by NSFC (Nos. 61170189, 61370126, 61202239), National High Technology Research and Development Program of China under grant No.2015AA016004, the Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2015ZX-16), and Microsoft Research Asia Fund (No. FY14-RES-OPP-105).

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Zhongyuan Wang & Ji-Rong Wen

  2. Microsoft Research, Beijing, China

    Zhongyuan Wang & Fang Wang

  3. State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

    Fang Wang & Zhoujun Li

Authors
  1. Zhongyuan Wang
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  2. Fang Wang
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  3. Ji-Rong Wen
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  4. Zhoujun Li
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Corresponding author

Correspondence to Zhongyuan Wang .

Editor information

Editors and Affiliations

  1. LIRMM, Montpellier Cedex 5, France

    Madalina Croitoru

  2. CRIL-CNRS, Lens, France

    Pierre Marquis

  3. Fakultät Informatik, Technische Universität Dresden, Dresden, Sachsen, Germany

    Sebastian Rudolph

  4. University of Brighton, Brighton, United Kingdom

    Gem Stapleton

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Wang, Z., Wang, F., Wen, JR., Li, Z. (2015). Bring User Interest to Related Entity Recommendation. In: Croitoru, M., Marquis, P., Rudolph, S., Stapleton, G. (eds) Graph Structures for Knowledge Representation and Reasoning. GKR 2015. Lecture Notes in Computer Science(), vol 9501. Springer, Cham. https://doi.org/10.1007/978-3-319-28702-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-28702-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28701-0

  • Online ISBN: 978-3-319-28702-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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