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arXiv · 2510.18364

Understanding Mobile App Recommendation Dynamics in General-Purpose LLMs: An Empirical Study

Quim Motger·Xavier Franch·Vincenzo Gervasi·Jordi Marco
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Abstract

Large Language Models (LLMs) are increasingly used to recommend mobile applications through natural language prompts, offering a flexible alternative to keyword-based app store search. Yet, the reasoning behind these recommendations remains opaque, raising questions about their consistency, explainability, and alignment with traditional App Store Optimization (ASO) metrics. In this paper, we present an empirical observational study of how general-purpose LLMs generate, justify, and rank mobile app recommendations across proprietary and open-source models, as well as knowledge-only settings with web search evaluated as a controlled ablation on the proprietary cohort. Our contributions are: (i) a taxonomy of 16 generalizable ranking criteria elicited from LLM outputs; (ii) a systematic evaluation framework to analyse recommendation consistency and the effect of explicit ranking instructions on cross-model convergence; and (iii) a replication package to support reproducibility and future research on LLM-based recommendation systems. Our findings reveal that LLMs report a broad yet fragmented set of ranking criteria, only partially aligned with standard ASO metrics. Proprietary models produce substantially more stable recommendations than locally deployed open-source models, and consistency varies substantially across app domains. Furthermore, enabling web search does not materially change the ranking criteria reported by LLMs. Contrary to our hypothesis, conditioning on explicit ranking criteria steers recommendations away from the blind baseline but reduces rather than increasing cross-model convergence. Our results aim to support end-users, app developers, and recommender-systems researchers in navigating the emerging landscape of conversational app discovery.

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Quim Motger, Xavier Franch, Vincenzo Gervasi, Jordi Marco. 2026-09-19. Understanding Mobile App Recommendation Dynamics in General-Purpose LLMs: An Empirical Study. https://arxiv.org/abs/2510.18364

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