53 citations · 258 across the 27 of their papers we have counts for
16 papers · 1 filter
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
Ninglu Shao, Jinshan Wang, Chenxu Wang +3
Currently, short video platforms have become the primary place for individuals to share experiences and obtain information. To better meet users' needs for acquiring information wh…
Unified Generative Search and Recommendation
Teng Shi, Jun Xu, Xiao Zhang +4
Modern commercial platforms typically offer both search and recommendation functionalities to serve diverse user needs, making joint modeling of these tasks an appealing direction.…
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
Teng Shi, Jun Xu, Xiao Zhang +4
Recently, the personalization of Large Language Models (LLMs) to generate content that aligns with individual user preferences has garnered widespread attention. Personalized Retri…
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors
Kepu Zhang, Zhongxiang Sun, Weijie Yu +5
Retriever-augmented generation (RAG) has become a widely adopted approach for enhancing the factual accuracy of large language models (LLMs). While current benchmarks evaluate the…
TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
Zihua Si, Lin Guan, ZhongXiang Sun +12
The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practi…
GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
Yimeng Bai, Yang Zhang, Fuli Feng +4
Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. H…
UniSAR: Modeling User Transition Behaviors between Search and Recommendation
Teng Shi, Zihua Si, Jun Xu +6
Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between us…
To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process
Zhongxiang Sun, Zihua Si, Xiao Zhang +4
Incorporating Search and Recommendation (S&R) services within a singular application is prevalent in online platforms, leading to a new task termed open-app motivation prediction,…
Large Language Models Enhanced Collaborative Filtering
Zhongxiang Sun, Zihua Si, Xiaoxue Zang +4
Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing w…
LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
Yimeng Bai, Yang Zhang, Jing Lu +5
Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has eme…
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
Zihua Si, Zhongxiang Sun, Jiale Chen +7
The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradig…
Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
Yang Zhang, Yimeng Bai, Jianxin Chang +6
With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recomme…
KuaiSAR: A Unified Search And Recommendation Dataset
Zhongxiang Sun, Zihua Si, Xiaoxue Zang +5
The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuit…
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
Zihua Si, Zhongxiang Sun, Xiao Zhang +5
Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been an…
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…
PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling
Xiaoxue Zang, Lijuan Liu, Maria Wang +3
We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in onlin emessaging. PhotoChat contains 12k dialogues, e…