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.
Bibliographical noteFunding Information:
This research is supported by research grant from the Key Program of National Natural Science Foundation of China (No.71631003).
- Heterogeneous network
- Paper recommendation
- Personalized recommendation