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.

对纸质推荐方法的先前研究考虑了历史用户的偏爱，很少能充分解决用户偏爱和兴趣的复杂性。我们提出了一种基于异构网络推荐个性化论文的方法，该异构网络包括论文，场所，作者，术语和用户以及这些实体之间的关系。我们调查网络中的元路径以捕获用户的偏好，并在这些元路径上应用随机游走，以衡量候选论文对目标用户的推荐分数。我们采用个性化的权重学习过程，以贝叶斯个性化排名为目标函数，在不同的元路径上发现用户的个性化权重。通过将不同元路径上的推荐分数与个性化权重相结合来计算全局推荐分数。