Semantic matchmaking systems are not successful in identifying the differences between users’ interests. To address this weakness, we develop a user-oriented and personalized system to evaluate the offers that match the user’s request. Our system evaluates and ranks the offers according to the user’s specific interests to bring better results to each individual. The best offer represents the maximum satisfaction of the user. The proposed system extracts and analyzes the user’s interests for multiple offer attributes. To evaluate the offers, we adapt the well-known economic model MultiNomial Logit to the field of semantic matchmaking. We show the benefits of our offer evaluation system through a detailed case study involving multiple, high-dimensional, concept and value-based attributes. In this study, we show how our system catches the differences between the purchasing interests of two buyers, and how it recommends a different best offer to each buyer. Furthermore, we assess the feasibility of the proposed system with a transport usage dataset. The experiment results demonstrate that our system can provide good result for each individual.