Related papers: The Impact of LLM-Generated Reviews on Recommender Systems: Textual Shifts, Performance Effects, and Strategic Platform Control

The Impact of LLM-Generated Reviews on Recommender Systems: Textual Shifts, Performance Effects, and Strategic Platform Control

URL: http://arxiv.org/abs/2601.02362v1
Date: Sun, 02 Nov 2025 09:06:47 GMT
Title: The Impact of LLM-Generated Reviews on Recommender Systems: Textual Shifts, Performance Effects, and Strategic Platform Control
Authors: Itzhak Ziv, Moshe Unger, Hilah Geva,
Abstract summary: The rise of generative AI technologies is reshaping content-based recommender systems (RSes)<n>This study examines how the introduction of AI-generated reviews influences RS performance and business outcomes.
Score: 0.4078247440919473
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The rise of generative AI technologies is reshaping content-based recommender systems (RSes), which increasingly encounter AI-generated content alongside human-authored content. This study examines how the introduction of AI-generated reviews influences RS performance and business outcomes. We analyze two distinct pathways through which AI content can enter RSes: user-centric, in which individuals use AI tools to refine their reviews, and platform-centric, in which platforms generate synthetic reviews directly from structured metadata. Using a large-scale dataset of hotel reviews from TripAdvisor, we generate synthetic reviews using LLMs and evaluate their impact across the training and deployment phases of RSes. We find that AI-generated reviews differ systematically from human-authored reviews across multiple textual dimensions. Although both user- and platform-centric AI reviews enhance RS performance relative to models without textual data, models trained on human reviews consistently achieve superior performance, underscoring the quality of authentic human data. Human-trained models generalize robustly to AI content, whereas AI-trained models underperform on both content types. Furthermore, tone-based framing strategies (encouraging, constructive, or critical) substantially enhance platform-generated review effectiveness. Our findings highlight the strategic importance of platform control in governing the generation and integration of AI-generated reviews, ensuring that synthetic content complements recommendation robustness and sustainable business value.

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