Incremental Set Recommendation Based on Class Differences

Shirai, Yasuyuki; Tsuruma, Koji; Sakurai, Yuko; Oyama, Satoshi; Minato, Shin-ichi

Incremental Set Recommendation Based on Class Differences

Shirai, Yasuyuki; Tsuruma, Koji; Sakurai, Yuko et al.

Lecture Notes in Computer Science, 2012, 7301, 183-194

Permalink : https://hdl.handle.net/2115/65257

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アクセス権	open access
URI	https://hdl.handle.net/2115/65257
タイトル	Incremental Set Recommendation Based on Class Differences
著者	Shirai, Yasuyuki ; Tsuruma, Koji ; Sakurai, Yuko ; Oyama, Satoshi ; Minato, Shin-ichi
著者名 Shirai, Yasuyuki
著者名 Tsuruma, Koji
e-Rad_Researcher 10396137 著者名 Sakurai, Yuko
e-Rad_Researcher 30346100 著者名 Oyama, Satoshi
e-Rad_Researcher 10374612 著者名 Minato, Shin-ichi
言語	英語
キーワード	recommendation
	classification
	collaborative filtering
	zero-suppressed binary decision diagram
発行日	2012
出版者	Springer
収録物名	Lecture Notes in Computer Science
巻(号)	7301
ページ	183-194
抄録	In this paper, we present a set recommendation framework that proposes sets of items, whereas conventional recommendation methods recommend each item independently. Our new approach to the set recommendation framework can propose sets of items on the basis on the user’s initially chosen set. In this approach, items are added to or deleted from the initial set so that the modified set matches the target classification. Since the data sets created by the latest applications can be quite large, we use ZDD (Zero-suppressed Binary Decision Diagram) to make the searching more efficient. This framework is applicable to a wide range of applications such as advertising on the Internet and healthy life advice based on personal lifelog data.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-30217-6_16
PISSN	0302-9743
関連情報	ISBN 978-3-642-30216-9
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1007/978-3-642-30217-6_16
関連情報 (isPartOf)	Title Advances in Knowledge Discovery and Data Mining, Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301), ISBN: 978-3-642-30216-9