Novel recommendation based on Personal Popularity Tendency

Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, Seung Taek Park

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

68 Citations (Scopus)


Recently, novel recommender systems have attracted considerable attention in the research community. Recommending popular items may not always satisfy users. For example, although most users likely prefer popular items, such items are often not very surprising or novel because users may already know about the items. Also, such recommender systems hardly satisfy a group of users who prefer relatively obscure items. Existing novel recommender systems, however, still recommend mainly popular items or degrade the quality of recommendation. They do so because they do not consider the balance between novelty and preference-based recommendation. This paper proposes an efficient novel-recommendation method called Personal Popularity Tendency Matching (PPTM) which recommends novel items by considering an individual's Personal Popularity Tendency (or PPT). Considering PPT helps to diversify recommendations by reasonably penalizing popular items while improving the recommendation accuracy. We experimentally show that the proposed method, PPTM, is better than other methods in terms of both novelty and accuracy.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Number of pages10
Publication statusPublished - 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: 2011 Dec 112011 Dec 14

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other11th IEEE International Conference on Data Mining, ICDM 2011
CityVancouver, BC

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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