摘要：AbstractDigitalization in the fashion industry is attracting the attention of both consumers and online shopping services. Therefore, a personalized and efficient recommendation system is becoming increasingly important. However, most traditional systems focus on recommendations without considering the outfit-user relationship, decreasing the accuracy of the recommendations. Therefore, we propose a fashion recommendation system based on user preferences. The adaptive system capacity is given by two phases. The first one generates a short-term memory that is constantly updated with the user’s interactions. The second one creates a long-term memory based on a DNN. The recommendation system is structured in 3 stages: Database Generator, Model Ranking, and implicit profiling. The Database Generator encodes the visual characteristics of the garments. The Ranking Model deals with the scoring of the recommendations. The implicit profiling updates the ranking according to the user’s preferences. Finally, the system is evaluated using images provided by the user. Through experiments based on user interaction, the system demonstrates adaptation capabilities by recommending similar outfits to the previous user selections. Thus, the proposed system demonstrated the ability to adjust to user preferences through human-machine interactions, as required for this recommendation system.
