21 citations · 56 across the 26 of their papers we have counts for
20 papers · 1 filter
Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
Yunjia Xi, Menghui Zhu, Jianghao Lin +4
Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into industrial RSs. While most appr…
A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
Yunjia Xi, Jianghao Lin, Yongzhao Xiao +7
The advent of Large Language Models (LLMs) has significantly revolutionized web search. The emergence of LLM-based Search Agents marks a pivotal shift towards deeper, dynamic, auto…
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Yunjia Xi, Jianghao Lin, Menghui Zhu +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding responses with retrieved information. As an emerging paradigm, Agentic RAG further enhances…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
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…
A Survey on Diffusion Models for Recommender Systems
Jianghao Lin, Jiaqi Liu, Jiachen Zhu +5
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inad…
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…
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…
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…
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…
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…
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
Jianghao Lin, Bo Chen, Hangyu Wang +7
Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID…
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to…
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi +11
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, con…
A Bird's-eye View of Reranking: from List Level to Page Level
Yunjia Xi, Jianghao Lin, Weiwen Liu +5
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface des…
Multi-Level Interaction Reranking with User Behavior History
Yunjia Xi, Weiwen Liu, Jieming Zhu +6
As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the impr…
Neural Re-ranking in Multi-stage Recommender Systems: A Review
Weiwen Liu, Yunjia Xi, Jiarui Qin +5
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby pla…
Context-aware Reranking with Utility Maximization for Recommendation
Yunjia Xi, Weiwen Liu, Xinyi Dai +5
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.…
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…