Incremental set recommendation based on class differences

Yasuyuki Shirai, Koji Tsuruma, Yuko Sakurai, Satoshi Oyama, Shin Ichi Minato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Number of pages12
EditionPART 1
Publication statusPublished - 2012
Event16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
Duration: May 29 2012Jun 1 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
CityKuala Lumpur

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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