🤖 AI Summary
This study addresses the challenge of information overload in massive product review corpora, which often impedes users from efficiently accessing personalized, salient insights. To tackle this issue, the authors propose an end-to-end framework for personalized review presentation that uniquely integrates aspect-level user preference modeling, multi-source sentiment-aware ranking, and large language model (LLM)-driven customized summarization. By leveraging fine-grained semantic matching and synthesizing sentiment signals, the approach effectively selects and summarizes reviews aligned with individual user interests. Extensive evaluation on an Amazon smartphone review dataset and a user study involving 70 participants demonstrates that the proposed method significantly outperforms existing baselines, yielding substantial improvements in relevance, user satisfaction, decision confidence, and reading efficiency.
📝 Abstract
Online consumer reviews play a crucial role in guiding purchase decisions by offering insights into product quality, usability, and performance. However, the increasing volume of user-generated reviews has led to information overload, making it difficult for consumers to identify content that aligns with their specific preferences. Existing review ranking systems typically rely on metrics such as helpfulness votes, star ratings, and recency, but these fail to capture individual user interests and often treat textual sentiment and rating signals separately. This research addresses these limitations by proposing a personalized framework that integrates review ranking and abstractive summarization to enhance decision-making efficiency. The proposed system begins by modeling each user's sentiment through a hybrid analysis of star ratings and review content. Simultaneously, user preferences were derived from historical reviews using sentence embeddings and clustering, forming semantic profiles aligned with thematic and sentiment dimensions. A relevance scoring algorithm matched these profiles with unseen reviews based on sentiment and aspect similarity. Top-matched reviews were then summarized to reflect individual interests. A user study with 70 participants demonstrated that the personalized approach improved satisfaction, perceived relevance, and decision-making confidence, while reducing time spent reading. The results highlight the method's effectiveness in alleviating information overload and delivering content tailored to user-specific preferences, emphasizing its value in enhancing user experience in review-rich decision-making environments.