Zhuoye Ding

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

2018

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 recommender systems consider the recommendation procedure as a static process …

Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

2019 · arXiv (Cornell University)

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 streaming setting provides users the interactive manner …

Automatic Product Copywriting for E-commerce

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying …

From Abstract to Details

2022 · Proceedings of the 30th ACM International Conference on Multimedia

In E-commerce recommendation, Click-Through Rate (CTR) prediction has been extensively studied in both academia and industry to enhance user experience and platform benefits. At present, most popular CTR prediction methods are concatenation-based models that represent …

Deep Reinforcement Learning for List-wise Recommendations

2017 · arXiv (Cornell University)

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 recommender systems consider the recommendation procedure as a static process …

Micro Behaviors

2018

The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. …

Deep reinforcement learning for page-wise recommendations

2018

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is - users are recommended a page …