Restaurant Menu Categorization at Scale: LLM -Guided Hybrid Clustering (2025)

Abstract: Inconsistent naming of menu items across merchants presents a major challenge for businesses that rely on large-scale menu item catalogs. It hinders downstream tasks like pricing analysis, menu item deduplication, and recommendations. To address this, we propose the Cross-Platform Semantic Alignment Framework (CPSAF), a hybrid approach that integrates DBSCAN-based clustering with SIGMA (Semantic Item Grouping and Menu Abstraction), a Large Language Model based refinement module. SIGMA employs in-context learning with a large language model to generate generic menu item names and categories. We evaluate our framework on a proprietary dataset comprising over 700,000 unique menu items. Experiments involve tuning DBSCAN parameters and applying SIGMA to refine clusters. The performance is assessed using both structural metrics i.e. cluster count, coverage and semantic metrics i.e. intra and inter-cluster similarity along with manual qualitative inspection. CPSAF improves intra-cluster similarity from 0.88 to 0.98 and reduces singleton clusters by 33%, demonstrating its effectiveness in recovering soft semantic drift.

## introduction in a highly competitive and rapidly evolving ecosystem, restaurants increasingly seek to expand their menus and align offerings with consumer preferences. traditionally, such decisions are based on manual market surveys and limited feasibility analysis. this mechanism is resource-intensive and lacks scalability. therefore, data-driven approaches can help restaurant owners to make informed decisions. a persistent challenge in this domain is the absence of standardized naming conventions across menu item listings. for instance, menu items such as "spicy chicken wrap" and "zinger wrap" may refer to identical products but are described differently across vendors or platforms. these inconsistencies hinder effective comparative analysis of menu data, limiting the ability to extract actionable insights. addressing this problem through semantically informed techniques can enable consistency among the products and enable a wide range of applications i.e. data-driven recommendation systems, competitive pricing strategies, and targeted business expansion. researchers have employed various clustering algorithms that group the menu items based on lexical or embedding-based similarity @xcite , @xcite . although the methods are effective to some extent, they often fail to represent deeper semantic relationships, leading to fragmented or imprecise clusters. recent advancements integrate large language models (llm) to improve clusters via prompting and few-shot learning @xcite , @xcite , @xcite , @xcite , @xcite . despite these improvements, current approaches overlook subtle name variations that occur across restaurants. consequently, they struggle to consistently align semantically equivalent menu items, limiting their utility in downstream analytical and recommendation tasks. to address these challenges, we propose the cross-platform semantic alignment framework (cpsaf), a hybrid pipeline that combines traditional clustering algorithms with llm-driven refinement. the contributions of this study are as follows: ## cross-platform semantic alignment framework (cpsaf) this study proposes a novel hybrid pipeline that combines a clustering algorithm and llm refinement to standardized menu item representations. its algorithm is given in algorithm 1. the proposed framework unfolds in two core stages: ## experimental design our evaluation framework measures cluster quality based on structural and semantic properties, as well as computation time. we first analyze baseline unsupervised models k-means, agglomerative, hierarchical dbscan (hdbscan), and dbscan focusing on runtime and metrics in ## ablation study to assess the contribution of individual components within the cpsaf framework, we conducted an ablation study by selectively removing or modifying key modules and measuring their impact on clustering performance. as shown in ## conclusion this paper addresses the challenge of identifying inconsistencies in menu item data and transforming them into actionable insights for business growth on food delivery platforms. we propose cpsaf, a cross-platform semantic alignment framework, which integrates dbscan-based clustering with llm-powered refinement (sigma) to enhance cluster accuracy, cohesion, and coverage. the framework incorporates a three-stage hierarchical assignment strategy to resolve textual inconsisten-cies between brands and merchants. we conducted extensive experiments across four clustering algorithms, varying dataset sizes and configurations to determine optimal dbscan parameters. it is followed by full-scale evaluations of 700,000 unique menu items. our hybrid approach achieves 100% cluster coverage, intra-cluster similarity of 0.98, and significantly lowers fragmentation compared to baseline dbscan. the analysis confirms that cpsaf produces structurally compact and semantically coherent clusters. these refined clusters enable downstream applications such as pricing analysis, brand recommendations, and merchantlevel targeting. in future work, our goal is to extend our system to support advanced features that benefit both customers and merchants. on the customer side, we plan to incorporate context-aware notifications, hyper-personalized recommendations, and natural language chatbot integrations to enhance user engagement and satisfaction. for merchants, our future efforts will focus on leveraging datadriven insights for menu optimization, targeted marketing, and strategic business intelligence, including geo-specific trends and competitive benchmarking.