A personalized paper recommendation method considering diverse user preferences

Yi Li, Ronghui Wang, Guofang Nan, Dahui Li, Minqiang Li

Research output: Contribution to journalArticlepeer-review

Abstract

Prior studies of paper recommendation methods that consider historical user preferences rarely adequately address the complexity of user preferences and interests. We propose a method to recommend personalized papers based on a heterogeneous network that includes papers, venues, authors, terms, and users as well as the relations among these entities. We investigate meta-paths in the network to capture user preferences and apply random walks on these meta-paths to measure recommendation scores of candidate papers to target users. We employ a personalized weight learning process to discover a user's personalized weights on different meta-paths using Bayesian Personalized Ranking as the objective function. A global recommendation score is calculated by combining recommendation scores on different meta-paths with personalized weights. We conducted experiments using two different datasets and the results showed that the proposed method performed better than other baseline methods.

Original languageEnglish (US)
Article number113546
JournalDecision Support Systems
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
This research is supported by research grant from the Key Program of National Natural Science Foundation of China (No. 71631003 ).

Publisher Copyright:
© 2021

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Heterogeneous network
  • Meta-paths
  • Paper recommendation
  • Personalized recommendation

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