Zhu Sun

TL;DR: RKGE is presented, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items and shows the superiority of RKGE against state-of-the-art methods.

TL;DR: An open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics is introduced, empirically finding that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.

TL;DR: A comprehensive and systematic survey of the recent research on recommender systems with side information can be found in this paper, where a number of recommendation algorithms have been proposed to leverage side information of users or items, demonstrating a high degree of effectiveness in improving recommendation performance.

TL;DR: This paper systematically review 85 recommendation papers published at eight top-tier conferences and creates benchmarks with standardized procedures and provides the performance of seven well-tuned state-of-the-arts across six metrics on six widely-used datasets as a reference for later study.

TL;DR: A comprehensive and systematic survey of the recent research on recommender systems with side information can be found in this paper, where a number of recommendation algorithms have been proposed to leverage side information of users or items, demonstrating a high degree of effectiveness in improving recommendation performance.