LLM -Rec: Personalized Recommendation via Prompting Large Language Models (2024)

Abstract: Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item. However, directly employing the original item descriptions may not yield optimal recommendation performance due to the lack of comprehensive information to align with user preferences. Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. In this study, we introduce a novel approach, coined LLM-Rec, which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations. Our empirical experiments reveal that using LLM-augmented text significantly enhances recommendation quality. Even basic MLP (Multi-Layer Perceptron) models achieve comparable or even better results than complex content-based methods. Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model’s comprehension of both general and specific item characteristics. This highlights the importance of employing diverse prompts and input augmentation techniques to boost the recommendation effectiveness of LLMs.

## introduction text-based recommendation systems exhibit a broad spectrum of applications, spanning across diverse domains and industries. this versatility mainly stems from the capability of natural language to effectively describe nearly any type of items, encompassing not only products, movies, and books but also news articles and user-generated content, including short videos and social media posts @xcite @xcite @xcite @xcite @xcite . nonetheless, these text-based recommendation systems are industrial_and_scientific 2 4 6 8 10 12 # important keywords in original descriptions frequently challenged by the inherent limitation of incomplete or insufficient information within item descriptions, which hinders the task of accurately aligning item characteristics with user preferences @xcite . the incompleteness may arise from two sources: a limited comprehension of the items themselves and an insufficient understanding of the users for whom recommendations are generated. this challenge is not confined only to domains with well-defined and categorized items (e.g., movies), but also extends to domains characterized by novel, unclassified, or less categorically structured items, as observed in the case of usergenerated content. in the context of movie recommendations, a movie's description usually include the main actors, and a brief plot summary. however, this limited information may not capture crucial elements like genre, tone, cinematography style, or thematic depth, resulting in less effective recommendations. as for user-generated content, imagine a social platform where users regularly post recipes which are often accompanied with brief textual descriptions like the name of the dish and a few ingredients, but limited details regarding preparation time, dietary restrictions, or flavor profiles. consider a user who follows a vegan diet and is interested in discovering new plant-based recipes. since the user-generated content often lacks comprehensive dietary information and may not explicitly mention terms like "vegan", "plant-based", or "vegetarian", in this scenario, the recommendation system, relying solely on the incomplete descriptions, may struggle to discern the veganfriendliness of the recipes. the recent advances in the development of large language models (llms) underscore their exceptional capacity to store comprehensive world knowledge @xcite @xcite , engage in complex reasoning @xcite , and function as versatile task solvers @xcite @xcite . in light of this advancement and recognizing the challenge posed by incomplete item descriptions, our study introduces the llm-rec framework. this approach is designed to exploit various prompting strategies to enrich input text with the intrinsic capabilities of llms for personalized recommendations. by leveraging llms, which have been fine-tuned on extensive language datasets @xcite , our goal is to unlock their potential in generating input text that is not only contextually aware but also of high quality, thereby elevating the overall recommendation quality. through comprehensive empirical experiments, we evaluate the effectiveness of the llm-rec framework. we find that integrating the augmented text as the new input achieves comparable or even superior recommendation performance compared to more advanced content-based recommendation approaches that rely solely on the original item descriptions. further in-depth analyses reveal that the devised prompting strategies prompt llms to generate words that represent both general and specific item characteristics. it is applicable in a diverse range of domains and is not limited to datasets with rich textual information (figure 1 ). our study provides insights into the impact of different prompting strategies on recommendation performance and sheds light on the potential of leveraging llms for personalized recommendation. ## llm-rec when composing a summary for recommendation purposes, it is customary to infuse it with specific emphases grounded in the author's comprehension of the movie. this might involve accentuating the movie's distinctive attributes that set it apart from other movies. for instance, one may opt to incorporate genre information as a crucial element for classifying the movie. however, the decision to leverage the concept of genre for enhancing the summary is predicated on the author's understanding that the genre is a meaningful construct, effectively aligning the summary with the preferences and expectations of the intended audience. this paper aims to explore the potential of large language models when prompted to generate informative item descriptions and subsequently how to leverage this augmented text for enhancing personalized recommendations. figure 2 shows the diagram of llm-rec. specifically, our study focuses on investigating four distinct llm prompting strategies for description enrichment, namely basic prompting, recommendation-driven prompting, engagement-guided prompting, and the combination of recommendation-driven and engagementguided prompting. the enriched text is then fed into the final recommendation module. ## discussions and conclusions in this study, we have investigated the effectiveness of llm-rec as a simple yet impactful mechanism for improving recommendation through llms. our approach is among those early attempts @xcite that leverage llms for text augmentation in recommendation. there are three key contributions that distinguish our work from the concurrent ones. first, while previous work, such as kar @xcite , focuses on design augmentation algorithm for a specific recommendation model, our model focuses on input text augmentation with llms, which is suitable for any content-based backbone recommendation models, demonstrating the flexibility of our approach. second, in addition to our recommendation-driven augmentation using llms, we also design engagement-guided prompts to augment the input, which contains more personalized item characteristics. overall, we conduct comprehensive experiments, with different combinations of prompting strategies, to not only illustrate the superior performance of our approach but also uncover the underlying rationale of the improvements. we introduce llm-rec, which enhances recommendation by augmenting the original item descriptions which often contains incomplete information for effective recommendations using large language models. we observed from extensive experiments that combining augmented input text and original item descriptions yields notable improvements in recommendation quality. these findings show the potential of using llms and strategic prompting techniques to enhance the accuracy and relevance of recommendation with an easier training process. by incorporating additional context, we enable the recommendation algorithms to capture more nuanced information and generate recommendations that better align with user preferences.