Advancing Performance of Retail Recommendation Systems

This paper presents two recommendation models, one traditional and one novel, for a retail men\u27s clothing company. J. Hilburn is a custom-fit, menswear clothing company headquartered in Dallas, Texas. J. Hilburn employs stylists across the United States, who engage directly with customers to assist in selecting clothes that fit their size and style. J. Hilburn tasked the authors of this paper to leverage data science techniques to the given data set to provide stylists with more insight into clients’ purchase patterns and increase overall sales. This paper presents two recommendation systems which provide stylists with automatic predictions about possible clothing interests of their clients. The first recommendation system is a commonly used content-based collaborative filtering model and serves as the base model to evaluate the second recommendation system. The second recommendation system is an ensemble model comprised of separate clustering, KNN, and time series models that is a novel approach. These models are then fed into a neural network in order to produce recommendations. These recommendations for J. Hilburn’s clients will hopefully lead to expanding their customer base and increasing their revenue as a result of more refined clothing and style recommendations. This paper describes the process of building two recommendation systems. Both models are evaluated using AUC as a metric as well as their potential for scalability. The ensemble model has a slightly higher AUC, 91\% versus 86\%. However, the ensemble model is computationally more extensive resulting in it requiring more resources to run

Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a "top'' (e.g. a shirt) and a "bottom'' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain

Outfit Recommender System

The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommendation system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user