89 citations · 90 across the 5 of their papers we have counts for
5 papers
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction
Qi Liu, Xingyuan Tang, Jianqiang Huang +9
Natural content and advertisement coexist in industrial recommendation systems but differ in data distribution. Concretely, traffic related to the advertisement is considerably spa…
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Qi Liu, Xuyang Hou, Defu Lian +4
Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better…
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Qi Liu, Xuyang Hou, Haoran Jin +6
Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency…
Deep Context Interest Network for Click-Through Rate Prediction
Xuyang Hou, Zhe Wang, Qi Liu +3
Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus…
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Yifang Qin, Yifan Wang, Fang Sun +6
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geograph…