Book Recommender System Using Linked Data for Improving Serendipity (特集 第27回(2019年度)年次大会(研究報告会&総会)) -- (第27回年次大会予稿) Book Recommender System Using Linked Data for Improving Serendipity

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Author(s)

Abstract

<p> Recent years, to overcome the flood of information, recommender systems (RSs) are being used in many scenarios, such as online shopping stores, movie website and so on. However, many recommender algorithms focus on accuracy based on a user profile, which may lead to reducing user's satisfaction. As high-accuracy based RSs suggest similar items that the user may have known before. As a result,the recommendation leads to hurt user's satisfaction. And there is a concept called serendipity which is a way to address this problem. This paper focuses on improving the serendipity of the RSs. We use two approaches, the variety of resources in Linked Data and author similarity. We extract author information from DBpedia dataset, which is one of the datasets in Linked Data. Then we calculate author similarity based on this information. This paper describes methods of book recommendations from the Book-Crossing dataset, reports recommendation results and discusses our recommender system according to our results.</p>

Journal

  • Joho Chishiki Gakkaishi

    Joho Chishiki Gakkaishi 29(2), 164-169, 2019

    Japan Society of Information and Knowledge

Codes

  • NII Article ID (NAID)
    130007668586
  • NII NACSIS-CAT ID (NCID)
    AN10459774
  • Text Lang
    ENG
  • ISSN
    0917-1436
  • NDL Article ID
    029766945
  • NDL Call No.
    Z14-1782
  • Data Source
    NDL  J-STAGE 
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