224 citations · 1.7k across the 40 of their papers we have counts for
9 papers · 1 filter
DGCN: Diversified Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Liang Chen +2
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the…
Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network
Zhenyu Han, Fengli Xu, Jinghan Shi +4
In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed n…
Group-Buying Recommendation for Social E-Commerce
Jun Zhang, Chen Gao, Depeng Jin +1
Group buying, as an emerging form of purchase in social e-commerce websites, such as Pinduoduo, has recently achieved great success. In this new business model, users, initiator, c…
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
Yu Zheng, Chen Gao, Xiang Li +3
Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users' conformity towards popular items, whic…
Bundle Recommendation with Graph Convolutional Networks
Jianxin Chang, Chen Gao, Xiangnan He +2
Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation b…
Price-aware Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Xiangnan He +2
In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes…
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data
Jingtao Ding, Guanghui Yu, Xiangnan He +2
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largel…
Recommender Systems with Characterized Social Regularization
Tzu-Heng Lin, Chen Gao, Yong Li
Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social ne…
Learning to Recommend with Multiple Cascading Behaviors
Chen Gao, Xiangnan He, Dahua Gan +7
Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Perfor…