Large language models (LLMs) are increasingly used in natural language processing tasks. Recommender systems traditionally use methods such as collaborative filtering and matrix factorization, as well as advanced techniques like deep learning and reinforcement learning. Although language models have been applied in recommendation, the recent trend have focused on leveraging the generative capabilities of LLMs for more personalized suggestions. While current research focuses on English due to its resource richness, this work explores the impact of non-English prompts on recommendation performance. Using OpenP5, a platform for developing and evaluating LLM-based recommendations, we expanded its English prompt templates to include Spanish and Turkish. Evaluation on three real-world datasets, namely ML1M, LastFM, and Amazon-Beauty, showed that usage of non-English prompts generally reduce performance, especially in less-resourced languages like Turkish. We also retrained an LLM-based recommender model with multilingual prompts to analyze performance variations. Retraining with multilingual prompts resulted in more balanced performance across languages, but slightly reduced English performance. This work highlights the need for diverse language support in LLM-based recommenders and suggests future research on creating evaluation datasets, using newer models and additional languages.
Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
New York University（纽约大学）
协同过滤推荐系统是在线市场和电子商务中的一个重要应用。然而，CF推荐已被证明遭受持续的问题，与稀疏的用户评级，这将进一步导致冷启动问题。现有方法通过应用标记级情感分析来解决数据稀疏问题，该标记级情感分析将文本评论转换为情感分数作为用户评级的补充。在本文中，我们试图优化情感分析与先进的NLP模型，包括BERT和RoBERTa，并实验是否CF推荐已进一步增强。我们在Amazon US Reviews数据集上构建了训练器，并使用传统的微调范式以及新的基于自适应的学习范式来调整预训练的BERT和RoBERTa。实验结果表明，基于RoBERTa预测的情感评分的推荐系统性能最优，与基线推荐系统相比，MAP、NDCG和K点的精确度提高了30.7%.基于情感分析的学习范式虽然在纯情感分析方面优于传统的微调范式，但不能进一步改进CF推荐系统。
Large Language Models are Zero-Shot Rankers for Recommender Systems
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；UC San Diego（加州大学圣迭戈分校）；School of Information, Renmin University of China（中国人民大学信息资源管理学院）；WeChat, Tencent（腾讯微信）
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Department of Communication, University of California, Los Angeles, CA 90095（通信系，加州大学洛杉矶分校，CA 90095）；Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104（计算机与信息科学系，宾夕法尼亚大学，Philadelphia, PA 19104）；Amenenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104（安纳伯格通信学院，宾夕法尼亚大学，Philadelphia, PA 19104）；Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA 19104（运营、信息与决策系，宾夕法尼亚大学，Philadelphia, PA 19104）
Recommender Systems in the Era of Large Language Models (LLMs)
The Hong Kong Polytechnic University（香港理工大学）；National University of Defense Technology（国防科技大学）；Michigan State University（密歇根州立大学）；Amazon（亚马逊）；City University of Hong Kong（香港城市大学）
