Bo Long

An Empirical Study on Recommendation with Multiple Types of Feedback

2016

User feedback like clicks and ratings on recommended items provides important information for recommender systems to predict users' interests in unseen items. Most systems rely on models trained using a single type of feedback, e.g., …

Deep Search Query Intent Understanding

2020 · arXiv (Cornell University)

Understanding a user's query intent behind a search is critical for modern search engine success. Accurate query intent prediction allows the search engine to better serve the user's need by rendering results from more relevant …

Sequential Search with Off-Policy Reinforcement Learning

2021

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time. Surprisingly, despite the …

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 …

Graph Neural Networks for Natural Language Processing: A Survey

2023 · Foundations and Trends® in Machine Learning

Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems …

Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce

2023 · arXiv (Cornell University)

Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user …

Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item, which often deviates from the real scenario, …