APPAREL RECOMMENDATION WITH TRANSFER LEARNING AND LOCALITY SENSITIVE HASHING

The textile and apparel industries have now grown a lot and there is a variety of clothing that is constantly renewed or changed throughout the world. Given the abundance of selection options available, we developed a system that takes an image a user provides and then offers a recommendation which matches the user’s query image. This study developed a cloth recommendation system, which employs transfer learning with a pre-trained deep learning model (VGG16) followed by locality sensitive hashing with random projection. The dataset was originated by the H&M company and was exhibited in a competition via Kaggle. This dataset contains 105K image data in total by addressing 130 different categories in five (5) main groups. Among a total of 7,000 of the Ladieswear group, occupying about 37.7% in the dataset, a balanced dataset was obtained by splitting the 7,000 images into seven (7) clothing groups. These groups are labeled dress, trousers, sweater, blouse, skirt, t-shirt, and vest top. Specifically, we extracted embedded features of the image using transfer learning and achieved a fast recommendation using locality sensitive hashing. We demonstrated the effectiveness of the proposed recommendation system by comparing the average cosine similarity of top 6 recommendations before and after locality sensitive hash. Furthermore, we qualitatively visualized the quality of the recommendation.