E-commerce content and collaborative-based recommendation using K-Nearest Neighbors and enriched weighted vectors
Bardia Rafieian, Marta R. Costa‐jussà · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2020
In this paper, we present two productive and functional recommender methods to improve the accuracy of predicting the right product for the user. One proposal is a survey-based recommender system that uses k-nearest neighbors. It recommends products by asking questions from the user, efficiently applying a binary product vector to the product attributes, and processing the request with a minimum error. The second proposal uses an enriched collaborative-based recommender system using enriched weighted vectors. Thanks to the style rules, the enriched collaborative based method recommends outfits with competitive recommendation quality. We evaluated both of the proposals on a Kaggle fashion-dataset along with iMaterialist and, results show equivalent performance on binary gender and product attributes.