LLMRS: Unlocking Potentials of LLM-Based Recommender Systems for Software Purchase

• URL: http://arxiv.org/abs/2401.06676v1
• Date: Fri, 12 Jan 2024 16:33:17 GMT
• Title: LLMRS: Unlocking Potentials of LLM-Based Recommender Systems for Software Purchase
• Authors: Angela John, Theophilus Aidoo, Hamayoon Behmanush, Irem B. Gunduz, Hewan Shrestha, Maxx Richard Rahman, Wolfgang Maa{\ss}
• Abstract summary: Large Language Models (LLM) offer promising results for analyzing user queries. We propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations.
• Score: 0.6597195879147557
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typically fail to capture user preferences and generate general recommendations. Recent advancements in Large Language Models (LLM) offer promising results for analyzing user queries. However, employing these models to capture user preferences and efficiency remains an open question. In this paper, we propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations. We experimented with LLMRS on a real-world dataset, the Amazon product reviews, for software purchase use cases. The results show that LLMRS outperforms the ranking-based baseline model while successfully capturing meaningful information from product reviews, thereby providing more reliable recommendations.

Related papers

• 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)
• Review-LLM: Harnessing Large Language Models for Personalized Review Generation [8.898103706804616]
  Large Language Models (LLMs) have shown superior text modeling and generating ability. We propose Review-LLM that customizes LLMs for personalized review generation.
  arXiv  Detail & Related papers  (2024-07-10T09:22:19Z)
• On Softmax Direct Preference Optimization for Recommendation [50.896117978746]
  We propose Softmax-DPO (S-DPO) to instill ranking information into the LM to help LM-based recommenders distinguish preferred items from negatives. Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders.
  arXiv  Detail & Related papers  (2024-06-13T15:16:11Z)
• Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning [57.523454568002144]
  Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools. We introduce ToolRec, a framework for LLM-empowered recommendations via tool learning. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. We consider two types of attribute-oriented tools: rank tools and retrieval tools.
  arXiv  Detail & Related papers  (2024-05-24T00:06:54Z)
• Unlocking the Potential of Large Language Models for Explainable Recommendations [55.29843710657637]
  It remains uncertain what impact replacing the explanation generator with the recently emerging large language models (LLMs) would have. In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework. By adopting several key fine-tuning techniques, controllable and fluent explanations can be well generated.
  arXiv  Detail & Related papers  (2023-12-25T09:09:54Z)
• LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
  The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated. We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
  arXiv  Detail & Related papers  (2023-08-23T16:32:54Z)
• GenRec: Large Language Model for Generative Recommendation [41.22833600362077]
  This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. GenRec uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our research underscores the potential of LLM-based generative recommendation in revolutionizing the domain of recommendation systems.
  arXiv  Detail & Related papers  (2023-07-02T02:37:07Z)
• UP5: Unbiased Foundation Model for Fairness-aware Recommendation [45.47673627667594]
  A growing concern that Large Language Models might inadvertently perpetuate societal stereotypes, resulting in unfair recommendations. This paper focuses on user-side fairness for LLM-based recommendation where the users may require a recommender system to be fair on sensitive features such as gender or age. We introduce a novel Counterfactually-Fair-Prompt (CFP) method towards Unbiased Foundation mOdels (UFO) for fairness-aware LLM-based recommendation.
  arXiv  Detail & Related papers  (2023-05-20T04:32:59Z)
• PALR: Personalization Aware LLMs for Recommendation [7.407353565043918]
  PALR aims to combine user history behaviors (such as clicks, purchases, ratings, etc.) with large language models (LLMs) to generate user preferred items. Our solution outperforms state-of-the-art models on various sequential recommendation tasks.
  arXiv  Detail & Related papers  (2023-05-12T17:21:33Z)
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach [83.62750225073341]
  We consider recommendation as instruction following by large language models (LLMs) We first design a general instruction format for describing the preference, intention, task form and context of a user in natural language. Then we manually design 39 instruction templates and automatically generate a large amount of user-personalized instruction data.
  arXiv  Detail & Related papers  (2023-05-11T17:39:07Z)
• Zero-Shot Next-Item Recommendation using Large Pretrained Language Models [16.14557830316297]
  We propose a prompting strategy called Zero-Shot Next-Item Recommendation (NIR) prompting that directs LLMs to make next-item recommendations. Our strategy incorporates a 3-step prompting that guides GPT-3 to carry subtasks that capture the user's preferences. We evaluate the proposed approach using GPT-3 on MovieLens 100K dataset and show that it achieves strong zero-shot performance.
  arXiv  Detail & Related papers  (2023-04-06T15:35:11Z)

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.