Visually-aware Recommendation System for Interior Design
Suitable recommendations are critical for a successful e-commerce experience, especially for product categories such as furniture. A well thought-out choice of furniture is decisive for the visual appearance and the comfort of a room. Interior design can take much time and not everyone is capable to do it. Some furniture stores offer recommendation systems on their website, which are usually based on collaborative filters that are very restrictive, can be inaccurate and require many data at first. This work aims to develop a method to provide set recommendations that adhere to a cohesive visual style. The method can automatically advise the user on what set of furniture to choose for a room around one seed piece. The proposed system uses a database where learned attributes of the dataset are previously stored. Once the user select a seed, the system extracts the attributes from the image to execute a query in the database. Finally, a visual search performed in the filtered subset will return the best candidates. This way has the advantage to receive the results faster and to reduce the searching space thereby improving efficiency. The system is presented that is both powerful and efficient enough to give useful user-specific recommendations in real-time.
Darmstadt, TU, Bachelor Thesis, 2020