Xiao Yu

TL;DR: This paper proposes to combine heterogeneous relationship information for each user differently and aim to provide high-quality personalized recommendation results using user implicit feedback data and personalized recommendation models.

TL;DR: This work proposes to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model.

TL;DR: This paper proposes to combine various relationship information from the network with user feedback to provide high quality recommendation results and uses meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network.

TL;DR: This work proposes to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta- path weights mutually enhance each other.

TL;DR: A novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network, and learns group memberships for objects and the significance of relevance features for each interest group by solving a joint optimization problem.