5 citations · 12 across the 25 of their papers we have counts for
17 papers · 1 filter
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu +7
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation method…
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Chenxu Zhu, Shigang Quan, Bo Chen +7
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While le…
Beyond Positive History: Re-ranking with List-level Hybrid Feedback
Muyan Weng, Yunjia Xi, Weiwen Liu +5
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level posit…
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language m…
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
Yang Yang, Bo Chen, Chenxu Zhu +6
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficultie…
Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
Jiachen Zhu, Jianghao Lin, Xinyi Dai +6
We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significant challenge in effectively enhan…
Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
Yunjia Xi, Hangyu Wang, Bo Chen +7
The past few years have witnessed a growing interest in LLM-based recommender systems (RSs), although their industrial deployment remains in a preliminary stage. Most existing depl…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +4
Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most exi…
ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation
Jizheng Chen, Kounianhua Du, Jianghao Lin +3
Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the int…
Large Language Models Make Sample-Efficient Recommender Systems
Jianghao Lin, Xinyi Dai, Rong Shan +4
Large language models (LLMs) have achieved remarkable progress in the field of natural language processing (NLP), demonstrating remarkable abilities in producing text that resemble…
DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation
Kounianhua Du, Jizheng Chen, Jianghao Lin +6
Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals…
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
Jingtong Gao, Bo Chen, Weiwen Liu +6
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused p…
FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past
Kounianhua Du, Jizheng Chen, Jianghao Lin +5
Recommender models play a vital role in various industrial scenarios, while often faced with the catastrophic forgetting problem caused by the fast shifting data distribution. To a…
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Huanshuo Liu, Bo Chen, Menghui Zhu +5
Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However…
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +5
The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current a…
Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
Hangyu Wang, Jianghao Lin, Bo Chen +4
The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec ari…