Non-personalized Fashion Outfit Recommendations
Nowadays the demand for compatible outfit recommendation system is rising up. There are a number of online fashion shops and web applications allowing their customers to observe collages of fashionable items which look good together. Such forms of recommendations help both clients and sellers. On one hand clients can make better choices while shopping and find new items of clothing that will match each other. On the other hand, complex outfit recommendations help sellers to sell more items which will influence their business KPIs (key performance indicators).
We can build fashion outfit recommendation system using various instruments: machine learning (mostly supervised learning), statistics, user modelling, rules construction and many others. The problem is that most of these techniques require a solid amount of data about users’ preferences and tastes to create good personal recommendations. But what if there is no such information? In this paper we are proposing the approach of creating fashion outfit recommendations in situations when we lack of that data (the cold start problem). We are going to share (1) the approach of dataset gathering and (2) the results of using external datasets and pre-trained models for fashion outfit recommendation creation.
KeywordsFashion outfit recommendations Recommender systems Machine learning Neural networks Transfer learning
This work has been completed at the chair of Algorithms and Technologies of Programming, Department of Innovation and High Technology, Moscow Institute of Physics and Technology (ATP DIHT MIPT).
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