New Framework Uses AI to Personalize Online Product Reviews for Better Shopping Decisions
Researchers have proposed a framework that combines aspect-level user preference modeling, sentiment analysis, and large language model summarization to rank and condense online product reviews for individual users. The system was evaluated on an Amazon Mobile Electronics dataset and tested with 70 participants across common consumer electronics categories. Results showed the approach outperformed five baseline ranking methods and improved user satisfaction, decision-making confidence, and reading efficiency.
A preprint posted to arXiv presents a personalized review ranking and summarization system designed to address information overload in e-commerce settings, where the sheer volume of consumer reviews can hinder rather than help purchasing decisions. The framework extracts aspect-level preferences and sentiment signals from a user's review history, then incorporates user-selected product aspects and written input to construct a personalized profile. Candidate reviews are scored by comparing this profile against aspect and sentiment representations at the review level, and the highest-ranked reviews are passed to a large language model for concise summarization. In evaluations using an Amazon Mobile Electronics dataset and a structured user study with 70 participants, the method outperformed random ordering, star-rating-based, helpfulness-vote-based, recency-based, and semantic-similarity-based ranking approaches. Participants reported improvements across multiple dimensions including perceived relevance, ease of finding information, and confidence in their decisions. The authors argue that combining aspect-level personalization, sentiment-aware ranking, and LLM-based summarization represents a more user-centered alternative to aggregate signals like star ratings or helpfulness votes.
What's missing
The study relies on a single product domain (Amazon Mobile Electronics), leaving generalizability to other categories (e.g., hospitality, apparel, or services) untested. The paper does not address potential privacy implications of building detailed user preference profiles from review histories, nor does it discuss how the system handles cold-start users with little or no review history. Long-term effects on decision quality — as opposed to perceived satisfaction — are not measured.
What different sources said
- arXiv cs.LGCenter
Improving User Experience with Personalized Review Ranking and Summarization
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