262 citations · 916 across the 30 of their papers we have counts for
30 papers
Discrete Conditional Diffusion for Reranking in Recommendation
Xiao Lin, Xiaokai Chen, Chenyang Wang +4
Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challeng…
A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue +5
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference…
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
Xiao Lin, Xiaokai Chen, Linfeng Song +3
An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch t…
State Regularized Policy Optimization on Data with Dynamics Shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4
In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…
Multi-Task Recommendations with Reinforcement Learning
Ziru Liu, Jiejie Tian, Qingpeng Cai +8
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard t…
Reinforcing User Retention in a Billion Scale Short Video Recommender System
Qingpeng Cai, Shuchang Liu, Xueliang Wang +6
Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, the…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Yuan Zhang, Xue Dong, Weijie Ding +3
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterat…
Two-Stage Constrained Actor-Critic for Short Video Recommendation
Qingpeng Cai, Zhenghai Xue, Chi Zhang +9
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially intera…
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Wanqi Xue, Qingpeng Cai, Zhenghai Xue +6
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric,…
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Chongming Gao, Shijun Li, Yuan Zhang +5
Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to addres…
Real-time Short Video Recommendation on Mobile Devices
Xudong Gong, Qinlin Feng, Yuan Zhang +5
Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on m…
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
Ruohan Zhan, Changhua Pei, Qiang Su +5
Watch-time prediction remains to be a key factor in reinforcing user engagement via video recommendations. It has become increasingly important given the ever-growing popularity of…
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Wanqi Xue, Qingpeng Cai, Ruohan Zhan +4
Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dw…
Constrained Reinforcement Learning for Short Video Recommendation
Qingpeng Cai, Ruohan Zhan, Chi Zhang +5
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and…
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Shiqi Wang, Shijun Li +6
While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that t…
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
Meng Ai, Biao Li, Heyang Gong +5
Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and p…
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei +6
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This is…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…
Graph Attention Collaborative Similarity Embedding for Recommender System
Jinbo Song, Chao Chang, Fei Sun +3
We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for r…
NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
Jinbo Song, Chao Chang, Fei Sun +2
Learning informative representations (aka. embeddings) of users and items is the core of modern recommender systems. Previous works exploit user-item relationships of one-hop neigh…