142 citations · 142 across the 1 of their papers we have counts for
4 papers
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Bin Liu, Ruiming Tang, Yingzhi Chen +3
Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models hav…
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Haokun Chen, Xinyi Dai, Han Cai +5
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-…
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…