Rethinking LLM -Based Recommendations: A Personalized Query-Driven Parallel Integration (2025)

Abstract: Recent studies have explored integrating large langucage models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture.To effectively address these issues, we propose a Query-to-Recommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the other. In this framework, LLMs are utilized to generate feature-enriched item descriptions and personalized user queries, allowing for capturing diverse preferences and enabling rich semantic matching in a zero-shot manner. To effectively combine the complementary strengths of LLM and collaborative signals, we introduce an adaptive reranking strategy. Extensive experiments demonstrate an improvement in performance up to 57%, while also improving the novelty and diversity of recommendations.

## introduction recommendation systems play a crucial role in delivering personalized content and service across various domains. as the demand for more accurate and diverse recommendations continues to increase, the integration of large language models (llm) has emerged as a promising advancement @xcite . llms possess extensive knowledge and exhibit remarkable abilities in understanding and generating text @xcite , enabling new opportunities to improve recommendation systems beyond the traditional collaborative filtering (cf) and content-based methods. recently, numerous studies have been proposed to leverage llm in recommendation systems @xcite @xcite . to harness the capabilities of llms for recommendation, two major research paradigms have emerged. the first line of works utilize llms as generative predictors, typically fine-tuning them on next-item prediction tasks. these methods generate textual representations (e.g., item titles) based on user histories and use them as queries for retrieving candidate items @xcite . while this approach leverages llms' generation strength, it requires fine-tuning, which introduces bias to train dataset and reduces diversity of recommendation. additionally, relying solely on item titles can limit the expressive capacity of llms, limiting their ability to align nuanced user preferences with rich item characteristics. the second line of research focuses on reranking candidate items directly within the llm prompt. in this setting, a separate candidate retrieval model selects a subset of items, which are then presented to the llm for scoring or reranking @xcite @xcite . these approaches typically leverage cf-based recommendation models such as sasrec @xcite for candidate selection @xcite , and various techniques have been proposed to align llms with cf signals @xcite . however, these approaches inherently depend on the performance of the candidate selector, and they limit the ability of llms to fully leverage their distinctive capabilities across the entire item pool. the information handled by the cf-based model and the llm is fundamentally different, and the current serialized architecture can limit performance due to the misalignment between these two components. this misalignment also becomes a bottleneck that hinders the utilization of the diverse knowledge embedded in llms for recommendation. in this study, we focus on addressing two key challenges in llm-based recommendation: the bias introduced by fine-tuning llms on recommendation datasets, and the bottleneck caused by the serialized architecture that utilizes cf-based models as candidate selectors. motivated by the fact that cf-based models and llms handle fundamentally different types of information, we propose a parallel framework that integrates both components without entangling their processes. our approach enables the generation of enriched, feature-aware queries that allow llms to effectively leverage their broad and diverse knowledge for recommendation. fig. 1 illustrates the difference between the traditional serialized approach and our proposed parallel approach. our proposed parallel architecture maximizes the utilization of the llm's world knowledge and creates synergy by effectively merging the two complementary perspectives. we argue that llms can offer a complementary perspective to cf-based recommendation models. to fully leverage their potential, llms need direct access to the global item pool; however, existing approaches that fine-tune llms to generate item titles hinder the utilization of the diverse knowledge of llms and introduce biases from the training dataset.. in our framework, llms and cf-based models independently retrieve top-k items from the entire candidate pool. by decoupling llms from preselected candidates, our method maximizes llms' ability and creates synergy between complementary perspectives. to utilize llms' rich world knowledge, we introduce item description expansion and personalized query generation where the llm generates feature-enriched item description and personalized user queries grounded in user histories and item characteristics. furthermore, we design an adaptive reranking strategy that aggregates the outputs of llms and cf-based models while maintaining their independency. unlike existing approaches that jointly train multiple models, our method dynamically balances contributions. our method not only improves the performance of the recommendation, but also promotes greater diversity and adaptability, effectively addressing the limitations of existing llm-based recommendation systems. the training-free nature of our framework allows for seamless incorporation of new items based on their key features without additional model updates, and it can also handle large, dynamically changing item pools. this advantage is particularly beneficial in real-world e-commerce scenarios, where large volumes of new items are continuously introduced and updated. consequently, our design significantly reduces both infrastructure costs and maintenance overhead, making the system highly scalable for rapidly evolving recommendation environments. our contributions in this work are as follows. ## method: querec framework in this section, we introduce querec(query-to-recommendation), a novel framework designed to leverage the llms through large item pool and to operate in parallel with cf-based models, enabling a dual-perspective that integrates both collaborative signals and language-based knowledge. fig. 2 illustrates the overall process of our method. the proposed method leverages llms to generate featureenriched item descriptions and personalized user queries, retrieving top-k items based on similarity scores between user queries and item descriptions. these llm-based results are then combined with the top-k items from a cf-based model, effectively fusing insights from both cf-based models and llms. ## experiment this section presents the experimental results and analysis that demonstrate the effectiveness of our proposed method. ## conclusion in this work, we introduced querec, a novel parallel framework that enhances the diversity and performance of recommendation by using llm to generate personalized queries. querec outperforms state-of-the-art baselines including llmbased reranking, llm-based next-item prediction, and cf-based methods, by effectively leveraging the rich knowledge embedded in llms. furthermore, our analysis highlights the ability of the method to reduce bias and increase the diversity of recommendations. by integrating the complementary strengths of llms and recommendation models, querec provides a promising direction for future recommendation research, offering a practical, training-free solution adaptable to evolving llms and large-scale dynamic item pools.