Temporal recommendation on graphs via long- and short-term preference fusion

Liang Xiang*, Quan Yuan, Shiwan Zhao, Li CHEN, Xiatian Zhang, Qing Yang, Jimeng Sun

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

273 Citations (Scopus)

Abstract

Accurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can often be determined by individual's long-term and short-term preferences. How to represent users' long-term and short-term preferences? How to leverage them for temporal recommendation? To address these challenges, we propose Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time. Based on the STG model framework, we propose a novel recommendation algorithm Injected Preference Fusion (IPF) and extend the personalized Random Walk for temporal recommendation. Finally, we evaluate the effectiveness of our method using two real datasets on citations and social bookmarking, in which our proposed method IPF gives 15%-34% improvement over the previous state-of-the-art.

Original languageEnglish
Title of host publicationKDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
Pages723-731
Number of pages9
DOIs
Publication statusPublished - 2010
Event16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010 - Washington, DC, United States
Duration: 25 Jul 201028 Jul 2010

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010
Country/TerritoryUnited States
CityWashington, DC
Period25/07/1028/07/10

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • Graph
  • Temporal recommendation
  • User preference

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