430 citations · 981 across the 16 of their papers we have counts for
7 papers · 1 filter
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
Xiaojie Guo, Shugen Wang, Hanqing Zhao +8
In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse,…
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
Xu Yuan, Hongshen Chen, Yonghao Song +4
Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still h…
User-Inspired Posterior Network for Recommendation Reason Generation
Haolan Zhan, Hainan Zhang, Hongshen Chen +4
Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user ex…
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Lixin Zou +2
With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a n…
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Lixin Zou, Long Xia, Zhuoye Ding +3
Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streami…
Deep Reinforcement Learning for Page-wise Recommendations
Xiangyu Zhao, Long Xia, Liang Zhang +3
Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction b…
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Xiangyu Zhao, Liang Zhang, Zhuoye Ding +3
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional reco…