Papers with Recommender systems
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation (2025.acl-industry)
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| Challenge: | Recommender systems are a key component of our day-to-day lives, but evaluation remains a challenge due to the gap between offline metrics and online behaviors. |
| Approach: | They propose a framework that enables users to build believable human proxies from historical data. |
| Outcome: | The proposed framework exhibits closer alignment with real humans than previous work, both at micro and macro levels. |
Siamese Neural Networks for Detecting Complementary Products (2021.eacl-srw)
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| Challenge: | e-commerce websites are often too overwhelming for users to find what they need at one place . a recommendation system can detect complementary products using only the purchase history . |
| Approach: | They propose a content-based recommender system for detecting complementary products using Siamese Neural Networks (SNN). |
| Outcome: | The proposed system detects complementary products with 85% accuracy using only the product titles. |
XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)
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| Challenge: | Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items. |
| Approach: | They propose a model-agnostic framework that enables large language models to provide comprehensive explanations for user behaviors in recommender systems. |
| Outcome: | The proposed framework outperforms baseline approaches in explainable recommender systems. |
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers (2024.acl-long)
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Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida, Zhiyu Chen, Giuseppe Castellucci, Nikhita Vedula, Jason Ingyu Choi, Shervin Malmasi
| Challenge: | Large Language Models (LLMs) have given rise to generative recommenders . however, improving the generated content through user feedback is prohibitively expensive . |
| Approach: | They propose a generative explore-exploit method that exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality. |
| Outcome: | The proposed approach exploits items with high engagement and actively explores hidden population preferences to improve recommendation quality. |
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)
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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao
| Challenge: | Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation. |
| Approach: | They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented . |
| Outcome: | The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines. |
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems (2025.findings-acl)
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| Challenge: | Existing approaches to align LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities. |
| Approach: | They propose a system that allows users to expand their vocabulary by assigning a unique ID to each item within the expanded vocabulary. |
| Outcome: | The proposed system maximizes the sequence understanding abilities of large language models, significantly enhancing their performance on recommendation tasks. |
Tunable LLM-based Proactive Recommendation Agent (2025.acl-long)
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| Challenge: | Current methods focus on catering to existing user interests, leading to polarized recommendation distributions. |
| Approach: | They propose an LLM-based Actor-Critic Agent framework to cultivate latent interests through multi-step recommendations. |
| Outcome: | The proposed framework optimizes long-term rewards and dynamically evolves with user feedback. |
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have produced significant advances in the field of recommender systems. |
| Approach: | They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources. |
| Outcome: | Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations. |