Hybrid ML Recommender System for Visually Similar Product Images - V, Bagyalakshmi; Sharma, Gaurav; Mahajan, Meghna; Ahmed, Muzzammil; Baishya, Kalyan Prakash; Abraham, Kuruvilla (International Journal of Computer Sciences and Engineering, 2021)
Fashion industry and innovation go hand in hand & technology could not be left far behind when it comes to innovation. Retail fashion is one of the early adopters of artificial intelligence when it comes to product development. AI based applications provides ease of search and shop for products. Either in the form of visual based search or suggesting products from same category with different attributes, retailers are providing every possible easement to customers for bet- ter shopping experience. With AI onboard, there is a huge infrastructure cost associated as well. In computer vision (AI), model training requires a good image data with labels & high-capacity platform for starters. Considering these facts, only using transformed feature vector of product images to generate clusters based on feature similarity can reduce the data de- pendency. Additionally, distance metric can be used to compute the feature distances & retrieval of top-k similar images by reverse indexing of image features to their corresponding images.