Book Recommender System Using Linked Data for Improving Serendipity
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- WENG Renlou
- Graduate School of Library, Information and Media Studies, University of Tsukuba
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- TAKAKU Masao
- Faculty of Library, Information and Media Science, University of Tsukuba
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抄録
<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>
収録刊行物
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- 情報知識学会誌
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情報知識学会誌 29 (2), 164-169, 2019-05-25
情報知識学会
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詳細情報 詳細情報について
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- CRID
- 1390845713080147584
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- NII論文ID
- 130007668586
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- NII書誌ID
- AN10459774
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- ISSN
- 18817661
- 09171436
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- HANDLE
- 2241/00157919
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- NDL書誌ID
- 029766945
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可