Book Recommender System Using Linked Data for Improving Serendipity

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...

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Bibliographic Details
Published inJoho Chishiki Gakkaishi Vol. 29; no. 2; pp. 164 - 169
Main Authors WENG, Renlou, TAKAKU, Masao
Format Journal Article
LanguageEnglish
Published Tokyo Japan Society of Information and Knowledge 25.05.2019
Japan Science and Technology Agency
Subjects
Algorithms
Author similarity
Book recommendation
Datasets
DBpedia
Linked Data
Recommender systems
Serendipity
Similarity
Websites
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Summary: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.
ISSN:0917-1436
1881-7661
DOI:10.2964/jsik_2019_031