Influential seed items recommendation

Qi Liu, Biao Xiang, Enhong Chen, Yong Ge, Hui Xiong, Tengfei Bao, Yi Zheng

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

18 Scopus citations

Abstract

In this paper, we present a systematic perspective study on choosing and evaluating the initial seed items that will be recommended to the cold start users. We first construct an item consumption correlation network to capture the existing users' general consumption behaviors. Then, we formalize initial items recommendation as the influential seed set selection problem. Along this line, we present several methods, each of which selects seed items according to different rules. Finally, the experimental results on two real-world data sets verify that with different seed items, the users' consumption numbers will be quite different. Meanwhile, the results also provide many deep insights into these selection methods and their recommended seed items.

Original languageEnglish (US)
Title of host publicationRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems
Pages245-248
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event6th ACM Conference on Recommender Systems, RecSys 2012 - Dublin, Ireland
Duration: Sep 9 2012Sep 13 2012

Publication series

NameRecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems

Conference

Conference6th ACM Conference on Recommender Systems, RecSys 2012
Country/TerritoryIreland
CityDublin
Period9/9/129/13/12

Keywords

  • Influential
  • Item network
  • Popularity
  • Seed items

ASJC Scopus subject areas

  • Software

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