5 citations · 5 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…