83 citations · 120 across the 10 of their papers we have counts for
11 papers
Tlow: Flow-based Item Tokenizer for Recommendation
Nian Li, Chonggang Song, Jingtao Ding +3
Item tokenizer encodes semantic embeddings into token IDs to replace the randomly assigned item IDs used in traditional recommendation models, fundamentally addressing the problems…
Alleviating Video-Length Effect for Micro-video Recommendation
Yuhan Quan, Jingtao Ding, Chen Gao +4
Micro-videos platforms such as TikTok are extremely popular nowadays. One important feature is that users no longer select interested videos from a set, instead they either watch t…
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi +3
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either…
Predicting the popularity of information on social platforms without underlying network structure
Wu Leilei, Yi Lingling, Ren Xiao-Long +1
The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, tradi…
Robust Preference-Guided Denoising for Graph based Social Recommendation
Yuhan Quan, Jingtao Ding, Chen Gao +3
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in s…
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Yu Zheng, Chen Gao, Jingtao Ding +4
Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the…
Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang, Fuli Feng +4
In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, s…
Graph Domain Adaptation: A Generative View
Ruichu Cai, Fengzhu Wu, Zijian Li +3
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is im…
Friend Network as Gatekeeper: A Study of WeChat Users' Consumption of Friend-Curated Contents
Quan Li, Zhenhui Peng, Haipeng Zeng +5
Social media enables users to publish, disseminate, and access information easily. The downside is that it has fewer gatekeepers of what content is allowed to enter public circulat…
SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems
Qiaoan Chen, Hao Gu, Lingling Yi +4
On social network platforms, a user's behavior is based on his/her personal interests, or influenced by his/her friends. In the literature, it is common to model either users' pers…
WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles
Quan Li, Ziming Wu, Lingling Yi +3
Social media, such as Facebook and WeChat, empowers millions of users to create, consume, and disseminate online information on an unprecedented scale. The abundant information on…