Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
(2404.00702)Abstract
Recommender systems aim to predict user interest based on historical behavioral data. They are mainly designed in sequential pipelines, requiring lots of data to train different sub-systems, and are hard to scale to new domains. Recently, LLMs have demonstrated remarkable generalized capabilities, enabling a singular model to tackle diverse recommendation tasks across various scenarios. Nonetheless, existing LLM-based recommendation systems utilize LLM purely for a single task of the recommendation pipeline. Besides, these systems face challenges in presenting large-scale item sets to LLMs in natural language format, due to the constraint of input length. To address these challenges, we introduce an LLM-based end-to-end recommendation framework: UniLLMRec. Specifically, UniLLMRec integrates multi-stage tasks (e.g. recall, ranking, re-ranking) via chain-of-recommendations. To deal with large-scale items, we propose a novel strategy to structure all items into an item tree, which can be dynamically updated and effectively retrieved. UniLLMRec shows promising zero-shot results in comparison with conventional supervised models. Additionally, it boasts high efficiency, reducing the input token need by 86% compared to existing LLM-based models. Such efficiency not only accelerates task completion but also optimizes resource utilization. To facilitate model understanding and to ensure reproducibility, we have made our code publicly available.
Overview
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UniLLMRec introduces an end-to-end Large Language Model (LLM) based recommendation framework, aiming to address scalability and domain adaptation challenges in traditional recommender systems.
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The framework features a hierarchically structured item tree for efficient item processing and a chain of recommendation tasks to utilize LLMs' zero-shot learning capability across various stages.
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UniLLMRec employs a Depth-first Search (DFS) strategy in its item tree to balance diversity and relevance in recommendations, enabling effective traversal and item recall.
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The framework demonstrates substantial efficiency gains and competitive performance metrics on benchmark datasets, showcasing the potential of LLMs in enhancing and streamlining recommender systems.
UniLLMRec: Bridging LLMs and Recommender Systems for End-to-End Efficiency
Introduction to the Concept of UniLLMRec
Recommender systems play a pivotal role in filtering vast sets of information to present users with items of interest. Traditional approaches require extensive data to train several models for different tasks within the recommendation pipeline, such as recall, ranking, and re-ranking, making them hard to adapt to new domains rapidly. LLMs have demonstrated the potential to generalize across diverse scenarios, suggesting their capacity to simplify and unify the recommendation process. However, integrating LLMs in recommender systems introduces challenges, especially in processing large-scale item datasets and executing multi-stage recommendation tasks efficiently.
To address these issues, this paper introduces UniLLMRec, an LLM-based end-to-end recommendation framework that operates without the need for discrete sub-systems or extensive retraining for domain adaptation. It innovatively incorporates a tree-structured item organization and a chain of recommendation tasks to tackle the scalability problem, efficiently handling large-scale item sets and performing zero-shot recommendations across various contexts.
The UniLLMRec Framework
UniLLMRec's architecture is strategically designed to navigate the challenges of large-item datasets and the integration of LLMs into recommender systems. It does so by implementing a hierarchically structured item tree for dynamic item processing and leveraging LLMs' capability for zero-shot learning to perform end-to-end recommendation tasks. The framework consists of several components:
- Item Tree Construction: A novel approach to structure large-scale items using a dynamically updatable tree. This method not only facilitates efficient traversal during the recall process but also significantly reduces the input token requirements by representing items in compact, semantically meaningful clusters.
- Chain-of-Recommendation Strategy: UniLLMRec performs recommendation tasks in a sequential chain, starting from user profile modeling to item recall and re-ranking. This innovative strategy allows leveraging the context and capabilities of LLMs across different stages of the recommendation process.
- Search Strategy with Item Tree: To manage the trade-off between diversity and relevance in recommendations, UniLLMRec employs a Depth-first Search (DFS) on the item tree. The DFS method ensures that items from various branches of the tree are considered, enhancing the diversity of recommendation results.
Through these components, UniLLMRec accomplishes a unified recommendation process that is efficient, scalable, and adaptable to new domains without the necessity for model retraining.
Experimentation and Results
The efficacy of UniLLMRec was assessed through comprehensive experiments on benchmark datasets like MIND and Amazon Review. The performance metrics employed include Recall, NDCG, and ILAD, focusing on the model's capability to recall relevant items and enhance recommendation diversity. Compared to conventional models and other LLM-based recommendation approaches, UniLLMRec exhibited substantial efficiency gains and competitive, if not superior, performance metrics. Notably, UniLLMRec, with its zero-shot capability, managed to perform on par with supervised models that underwent extensive training on sizable datasets.
Implications and Future Directions
UniLLMRec represents a significant step towards integrating LLMs into the recommendation systems efficiently. It addresses practical challenges, including scalability and dynamic adaptability, showcasing the potential of LLMs to streamline and enhance recommender systems. The framework's success opens avenues for further research into optimizing item tree structures for improved performance and exploring deeper integration of LLM capabilities in understanding user preferences and item semantics. Future studies could also look into applying UniLLMRec's principles across different domains beyond text-based recommendations, expanding the applicability of LLMs in recommendation systems.
Conclusion
UniLLMRec addresses the critical challenges of integrating LLMs into scalable, efficient, and end-to-end recommendation systems. By dynamically structuring item data and leveraging the zero-shot capabilities of LLMs, it achieves competitive performance across multiple recommendation tasks. This research not only provides a novel framework for recommendations but also contributes to the broader dialogue on the application of LLMs in diverse practical scenarios, laying the groundwork for future advancements in the field.