arXiv:2601.05261v2 Announce Type: replace-cross Abstract: Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information overload and makes it difficult for users to identify content that matches their individual preferences. Existing review-ranking approaches commonly rely on aggregate signals such as star ratings, helpfulness votes, or recency, which may not reflect user-specific interests. This paper proposes a personalized review ranking and summarization framework that integrates user preference modeling, hybr
Source: arXiv cs.LG — read the full report at the original publisher.