430 citations · 981 across the 16 of their papers we have counts for
16 papers
Automatic Scene-based Topic Channel Construction System for E-Commerce
Peng Lin, Yanyan Zou, Lingfei Wu +3
Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this…
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,…
Automatic Product Copywriting for E-Commerce
Xueying Zhang, Yanyan Zou, Hainan Zhang +10
Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product character…
Adaptive Bridge between Training and Inference for Dialogue
Haoran Xu, Hainan Zhang, Yanyan Zou +3
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scena…
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Xu Wang, Hainan Zhang, Shuai Zhao +5
Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skil…
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
Junpeng Liu, Yanyan Zou, Hainan Zhang +4
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In…
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…
Probing Product Description Generation via Posterior Distillation
Haolan Zhan, Hainan Zhang, Hongshen Chen +5
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks.…
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…
Group-wise Contrastive Learning for Neural Dialogue Generation
Hengyi Cai, Hongshen Chen, Yonghao Song +4
Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning.…
Collaborative Group Learning
Shaoxiong Feng, Hongshen Chen, Xuancheng Ren +3
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically str…
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…
Deep Reinforcement Learning for List-wise Recommendations
Xiangyu Zhao, Liang Zhang, Long Xia +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…