Llm-esr: Large language models enhancement for long-tailed sequential recommendation
Sequential recommender systems (SRS) aim to predict users' subsequent choices based on
their historical interactions and have found applications in diverse fields such as e …
their historical interactions and have found applications in diverse fields such as e …
All roads lead to rome: Unveiling the trajectory of recommender systems across the llm era
Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …
personalized content, responding to users' diverse information needs. The emergence of …
SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations
Large language models (LLMs) have attracted significant attention in recommendation
systems. Current LLM-based recommender systems primarily rely on supervised fine-tuning …
systems. Current LLM-based recommender systems primarily rely on supervised fine-tuning …
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
Urban socioeconomic indicator prediction aims to infer various metrics related to sustainable
development in diverse urban landscapes using data-driven methods. However, prevalent …
development in diverse urban landscapes using data-driven methods. However, prevalent …
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
The integration of Large Language Models (LLMs) into recommender systems has led to
substantial performance improvements. However, this often comes at the cost of diminished …
substantial performance improvements. However, this often comes at the cost of diminished …
Enhancing Recommendation Explanations through User-Centric Refinement
Generating natural language explanations for recommendations has become increasingly
important in recommender systems. Traditional approaches typically treat user reviews as …
important in recommender systems. Traditional approaches typically treat user reviews as …
CoRNStack: High-Quality Contrastive Data for Better Code Ranking
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and
software maintenance, particularly as software systems increase in complexity. While current …
software maintenance, particularly as software systems increase in complexity. While current …
Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking
C Wang, X Wei, Y Jiang, F Ong, K Gao, X Yu… - arxiv preprint arxiv …, 2025 - arxiv.org
With the vast and dynamic user-generated content on Roblox, creating effective game
recommendations requires a deep understanding of game content. Traditional …
recommendations requires a deep understanding of game content. Traditional …
Recommender Systems Meet Large Language Model Agents: A Survey
In recent years, the integration of Large Language Models (LLMs) and Recommender
Systems (RS) has revolutionized the way personalized and intelligent user experiences are …
Systems (RS) has revolutionized the way personalized and intelligent user experiences are …
CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and
software maintenance, particularly as software systems increase in complexity. While current …
software maintenance, particularly as software systems increase in complexity. While current …