Incremental set recommendation based on class differences

Authors:
Yasuyuki Shirai;Koji Tsuruma;Yuko Sakurai;Satoshi Oyama;Shin-ichi Minato
Affiliations:
JST-ERATO MINATO Discrete Structure Manipulation System Project, Hokkaido University, Sapporo, Japan;JST-ERATO MINATO Discrete Structure Manipulation System Project, Hokkaido University, Sapporo, Japan;Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan;JST-ERATO MINATO Discrete Structure Manipulation System Project, Hokkaido University, Sapporo, Japan,Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

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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.