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Incremental Set Recommendation Based on Class Differences
Title: | Incremental Set Recommendation Based on Class Differences |
Authors: | Shirai, Yasuyuki Browse this author | Tsuruma, Koji Browse this author | Sakurai, Yuko Browse this author →KAKEN DB | Oyama, Satoshi Browse this author →KAKEN DB | Minato, Shin-ichi Browse this author →KAKEN DB |
Keywords: | recommendation | classification | collaborative filtering | zero-suppressed binary decision diagram |
Issue Date: | 2012 |
Publisher: | Springer |
Citation: | 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 |
Journal Title: | Lecture Notes in Computer Science |
Volume: | 7301 |
Start Page: | 183 |
End Page: | 194 |
Publisher DOI: | 10.1007/978-3-642-30217-6_16 |
Abstract: | 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. |
Rights: | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-30217-6_16 |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/65257 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小山 聡
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