192 citations · 378 across the 13 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.IR2019★ 192 cited
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
Zhu Sun, Qing Guo, Jie Yang +4
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…
cs.IR2019★ 6 cited
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
Fajie Yuan, Xiangnan He, Haochuan Jiang +4
Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or…
cs.IR2019★ 75 cited
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
Hui Fang, Danning Zhang, Yiheng Shu +1
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov c…