8 citations · 39 across the 52 of their papers we have counts for
12 papers · 1 filter
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
Jiahong Zhou, Shunhui Mao, Guoliang Yang +5
Recommender systems aim to recommend the most suitable items to users from a large number of candidates. Their computation cost grows as the number of user requests and the complex…
HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning
Hao Wang, Bo Tang, Chi Harold Liu +7
Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day. The bidding strategy hand…
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation
Junyan Qiu, Haitao Wang, Zhaolin Hong +3
The successful integration of large language models (LLMs) into recommendation systems has proven to be a major breakthrough in recent studies, paving the way for more generic and…
TBIN: Modeling Long Textual Behavior Data for CTR Prediction
Shuwei Chen, Xiang Li, Jian Dong +3
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations. Inspired by the recent thriving of language models (LMs), a surge of works improve predi…
Cross-Element Combinatorial Selection for Multi-Element Creative in Display Advertising
Wei Zhang, Ping Zhang, Jian Dong +5
The effectiveness of ad creatives is greatly influenced by their visual appearance. Advertising platforms can generate ad creatives with different appearances by combining creative…
A Collaborative Transfer Learning Framework for Cross-domain Recommendation
Wei Zhang, Pengye Zhang, Bo Zhang +2
In the recommendation systems, there are multiple business domains to meet the diverse interests and needs of users, and the click-through rate(CTR) of each domain can be quite dif…
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
Huinan Sun, Guangliang Yu, Pengye Zhang +3
Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising syst…
Safe Offline Reinforcement Learning with Real-Time Budget Constraints
Qian Lin, Bo Tang, Zifan Wu +5
Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years. However, most existing works i…
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
Xiaowen Shi, Ze Wang, Yuanying Cai +6
Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and the…
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Xiaowen Shi, Fan Yang, Ze Wang +6
Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands.…
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
Jian Dong, Yisong Yu, Yapeng Zhang +6
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understandi…
Decision-Making Context Interaction Network for Click-Through Rate Prediction
Xiang Li, Shuwei Chen, Jian Dong +4
Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems. Existing methods usually model user behaviors, while ignoring the informative conte…