How Acquiring the “Perfect Match” within Clothing Recommendation Systems Relies on Human Judgement: Literature Review
Haverford College. Department of Computer Science
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Dark Archive until 2029-01-01, afterwards Open Access.
Recommendation systems exist in almost every aspect of online business. Whether the system is recommending books, movies, clothes, etc., an algorithm is running behind the scenes. These algorithms are hard to acquire in pseudocode because they are generally proprietary but I will analyze one algorithm found in my research that could hint at a recommendation algorithm’s general nature. Many such recommender algorithms implement some form of collaborative filtering, which utilizes historical user data, clustered product data, and user spending patterns to identify specific recommendations. Clothing recommendation systems are becoming increasingly more complex as they not only have to account for matching clothing products to user patterns and trends, but also the ability to analyze the product images themselves to match attributes of color, style, and clothing type to the user’s preferences. I will be focusing on clothing recommender systems with an ultimate suggestion on a new format that could potentially solve the problems of cold start (i.e. problem when algorithm does not have enough user data to begin with) and fashion style consistency (i.e. problem when fashion genres or clothing types are inconsistent skewing the algorithm’s ability to recommend products that match).