学术文献库

A successful real estate search process involves locating a property that meets a user's search criteria subject to an allocated budget and time constraints. Many studies have investigated modeling housing prices over time. However, little is known about how a user's tastes influence their real estate search and purchase decisions. It is unknown what house a user would choose taking into account an individual's personal tastes, behaviors, and constraints, and, therefore, creating an algorithm that finds the perfect match. In this paper, we investigate the first step in understanding a user's tastes by building a system to capture personal preferences. We concentrated our research on real estate photos, being inspired by house aesthetics, which often motivates prospective buyers into considering a property as a candidate for purchase. We designed a system that takes a user-provided photo representing that person's personal taste and recommends properties similar to the photo available on the market. The user can additionally filter the recommendations by budget and location when conducting a property search. The paper describes the application's overall layout including frontend design and backend processes for locating a desired property. The proposed model, which serves as the application's core, was tested with 25 users, and the study's findings, as well as some key conclusions, are detailed in this paper.