2 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking
Chuang Li, Weida Liang, Hengchang Hu +4
We tackle the challenge of integrating large language models (LLMs) with external recommender systems to enhance domain expertise in conversational recommendation (CRS). Current LL…
Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Qijiong Liu, Jieming Zhu, Lu Fan +5
In recent years, integrating large language models (LLMs) into recommender systems has created new opportunities for improving recommendation quality. However, a comprehensive benc…
Vector Quantization for Recommender Systems: A Review and Outlook
Qijiong Liu, Xiaoyu Dong, Jiaren Xiao +6
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decad…
Discrete Semantic Tokenization for Deep CTR Prediction
Qijiong Liu, Hengchang Hu, Jiahao Wu +3
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.…
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
Hengchang Hu, Qijiong Liu, Chuang Li +1
In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate such conc…
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Jiahao Wu, Qijiong Liu, Hengchang Hu +5
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on…