The Application of Large Language Models in Recommendation Systems

• URL: http://arxiv.org/abs/2501.02178v2
• Date: Fri, 17 Jan 2025 08:44:57 GMT
• Title: The Application of Large Language Models in Recommendation Systems
• Authors: Peiyang Yu, Zeqiu Xu, Jiani Wang, Xiaochuan Xu,
• Abstract summary: Large Language Models are powerful tools that enable recommendation frameworks to tap into unstructured data sources. This work discusses applications of LLMs in recommendation systems, especially in electronic commerce, social media platforms, streaming services, and educational technologies.
• Score: 0.0
• License:
• Abstract: The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of recommendations, such as collaborative filtering and content-based filtering, are seriously limited in the solution of cold-start problems, sparsity of data, and lack of diversity in information considered. LLMs, of which GPT-4 is a good example, have emerged as powerful tools that enable recommendation frameworks to tap into unstructured data sources such as user reviews, social interactions, and text-based content. By analyzing these data sources, LLMs improve the accuracy and relevance of recommendations, thereby overcoming some of the limitations of traditional approaches. This work discusses applications of LLMs in recommendation systems, especially in electronic commerce, social media platforms, streaming services, and educational technologies. This showcases how LLMs enrich recommendation diversity, user engagement, and the system's adaptability; yet it also looks into the challenges connected to their technical implementation. This can also be presented as a study that shows the potential of LLMs for changing user experiences and making innovation possible in industries.

Related papers

• Enhanced Recommendation Combining Collaborative Filtering and Large Language Models [0.0]
  Large Language Models (LLMs) provide a new breakthrough for recommendation systems. This paper proposes an enhanced recommendation method that combines collaborative filtering and LLMs. The results show that the hybrid model based on collaborative filtering and LLMs significantly improves precision, recall, and user satisfaction.
  arXiv  Detail & Related papers  (2024-12-25T00:23:53Z)
• LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation [47.34949656215159]
  Large Language Models (LLMs) can be considered databases with a wealth of knowledge learned from the web data. We propose a LLM's Intuition-aware Knowledge graph Reasoning model (LIKR) Our model outperforms state-of-the-art recommendation methods in cold-start sequential recommendation scenarios.
  arXiv  Detail & Related papers  (2024-12-17T01:52:15Z)
• Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning [57.28766250993726]
  This work explores adapting to dynamic user interests without any model updates. Existing Large Language Model (LLM)-based recommenders often lose the in-context learning ability during recommendation tuning. We propose RecICL, which customizes recommendation-specific in-context learning for real-time recommendations.
  arXiv  Detail & Related papers  (2024-10-30T15:48:36Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations [19.405233437533713]
  Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation. We introduce a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
  arXiv  Detail & Related papers  (2024-03-05T08:31:00Z)
• Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis [91.5632751731927]
  Large Language Models such as ChatGPT have showcased remarkable abilities in solving general tasks. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. We analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results.
  arXiv  Detail & Related papers  (2024-01-10T08:28:56Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.