The primary goal of tourism management platforms, e.g. booking.com, is to provide the best matches to travelers. User-generated reviews are often leveraged in various ways to influence user’s decision-making process. One straightforward approach is ranking historical reviews based on user’s preferences by checking the “helpfulness” votes. A major issue with this approach is that many reviews do not receive helpfulness votes, leading to a presentation bias. In this work, we incorporate multiple review features to rank reviews based on user profiles. Reviews are encoded using a state-of-the-art transformer encoder model (e.g., SBERT), and cosine similarity is computed between user profiles and reviews. The ranking performance is assessed with MRR@10 (Mean Reciprocal Rank) and Precision@10. Our results demonstrate that beyond helpfulness votes, leveraging additional features (e.g., accommodation type, review title, positive aspects of reviews, etc.) significantly improves performance. The implementation of our method is available on the GitHub