542 citations · 744 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Feng Liu, Ruiming Tang, Xutao Li +5
Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regressio…
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
Huifeng Guo, Ruiming Tang, Yunming Ye +2
The KNN approach, which is widely used in recommender systems because of its efficiency, robustness and interpretability, is proposed for session-based recommendation recently and…
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +3
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias…
Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
Feng Liu, Ruiming Tang, Xutao Li +3
Recently, some studies have utilized the Markov Decision Process for diversifying (MDP-DIV) the search results in information retrieval. Though promising performances can be delive…
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +2
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a str…
A Graph-based Push Service Platform
Huifeng Guo, Ruiming Tang, Yunming Ye +2
It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line syst…