Walsh, GregSummers, KathrynKreisel, Amber2019-05-312019-05-312019-05-30UB_2019_Kreisel_Ahttp://hdl.handle.net/11603/13992Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Data is being collected like never before, through advancing technologies and enhanced data scraping techniques. Here, we will look at a connection of data from a person’s personal closet and shopping habits, combined with their social and business calendar to make predictive recommendations to users on their next purchase. Data exists in each person’s shopping habits and personal wardrobes. If this data is collected, extracted and combined then connected with recommender systems the outcome would have an effect on the way a consumer shops. By extracting existing data from clothing already in a person’s closet, combining it with personal preferences such as cost, quality and style and connecting it to a person’s social calendar- the shopping experience could be forever changed. By using Artificial Intelligence and recommender systems, accurate suggestions could be made to users before they even knew they needed a new article of clothing. Using data to predict when a person would be likely to purchase something and or recommending purchases based on a set of like parameters would enhance the users shopping experience.84 pagesen-USComputer Science - Machine LearningOnline retailingIntuitive IntelligenceShoppingData AnalyticsData CollectionText