65 citations · 167 across the 11 of their papers we have counts for
13 papers
A Graph-Enhanced Click Model for Web Search
Jianghao Lin, Weiwen Liu, Xinyi Dai +6
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models…
Retrieval & Interaction Machine for Tabular Data Prediction
Jiarui Qin, Weinan Zhang, Rong Su +5
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structu…
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Weiwen Liu, Feng Liu, Ruiming Tang +3
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS),…
An Adversarial Imitation Click Model for Information Retrieval
Xinyi Dai, Jianghao Lin, Weinan Zhang +7
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback. Click models, which study how use…
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
Nasim Sonboli, Farzad Eskandanian, Robin Burke +2
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fa…
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation
Feng Liu, Weiwen Liu, Xutao Li +1
Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling th…
Consistency-Aware Recommendation for User-Generated ItemList Continuation
Yun He, Yin Zhang, Weiwen Liu +1
User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or"boards") on Pinterest, book-based lists on Goodrea…
Contextual Combinatorial Conservative Bandits
Xiaojin Zhang, Shuai Li, Weiwen Liu +1
The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range…
Wasserstein Collaborative Filtering for Item Cold-start Recommendation
Yitong Meng, Guangyong Chen, Benben Liao +2
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To…
PMD: An Optimal Transportation-based User Distance for Recommender Systems
Yitong Meng, Xinyan Dai, Xiao Yan +5
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot full…
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh +9
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of…
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh +2
Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during wh…
Personalizing Fairness-aware Re-ranking
Weiwen Liu, Robin Burke
Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender sys…