45 citations · 118 across the 22 of their papers we have counts for
16 papers · 1 filter
Uncertainty as Remedy: Mitigating Satisfaction Label Bias in Short Video Multi-Objective Ensemble Ranking
Zonghe Shao, Tiantian He, Xiaoxiao Xu +6
The core objective of short video recommendation is to model users' unobservable true satisfaction with recommended videos. As the dominant industrial framework, end-to-end multi-o…
HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems
Xiao Liang, Jiali Feng, Xin Feng +10
With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms…
Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
Le Zhang, Xiaolan Zhu, Yuchen Wang +9
As live streaming services grow, many platforms offer short videos and live streams to meet diverse needs. Short videos carry substantial traffic and rich behavior signals, whereas…
Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
Xiaoxiao Xu, Hao Wu, Wenhui Yu +3
We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output a…
Creator-Side Recommender System: Challenges, Designs, and Applications
Xiaoshuang Chen, Yibo Wang, Yao Wang +4
Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's req…
Modeling User Fatigue for Sequential Recommendation
Nian Li, Xin Ban, Cheng Ling +6
Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed…
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
Kai Zheng, Haijun Zhao, Rui Huang +6
The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's…
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
Ziru Liu, Shuchang Liu, Zijian Zhang +6
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-t…
Future Impact Decomposition in Request-level Recommendations
Xiaobei Wang, Shuchang Liu, Xueliang Wang +6
In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term perform…
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
Yunzhu Pan, Chen Gao, Jianxin Chang +5
Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditiona…
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…
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Weiqi Zhao, Dian Tang, Xin Chen +5
Recommender systems usually rely on observed user interaction data to build personalized recommendation models, assuming that the observed data reflect user interest. However, user…
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
Jianxin Chang, Chenbin Zhang, Zhiyi Fu +8
Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction s…
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
Jianxin Chang, Chenbin Zhang, Yiqun Hui +4
With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challen…
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…