E -Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors (2020)

Abstract: In this paper, we present two productive and functional recommender methods to improve the ac- curacy 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.

## introduction the demanding market of the fashion industry has led to a challenging environment for recommender systems @xcite . to say, increasing information in the fashion shopping brings confusion to users who have to select the right choice among a huge number of available fashion products. furthermore, to satisfy the excessive desire of users in the fashion industry, recommendation engines are playing an important role by automating the product selection procedure @xcite . a recommender system targets to predict user's tastes and recommend product items that exceptionally are interesting for them. in recommendation engines, the product and the user information is gathered to predict the score or choice of a user to a product. the information collected from a user is either directly or indirectly while the data for products is collected explicitly. in content-based filtering recommendations @xcite , we explicitly gather information for both users and products and then compare the similarity to recommend the best choice. on the other hand, the collaborative-based algorithm @xcite uses "user behavior" for recommending items. they explore the behavior of users and products in terms of rating, selection, purchase history, and cookies @xcite . we introduce two settings of recommendation systems for the fashion industry using both types of collaborative and content-based filtering. in the first approach, we propose a survey content-based recommender system and as a second, we introduce an enriched collaborative-based recommender using a novel weighted system. finally, we initiate style rules that bring simplicity in selecting outfits based on available products. the rest of the paper is organized as follows. the next section covers an overview of available approaches in fashion recommending systems. section 3 describes our proposed methods in detail. section 4 shows the experimental framework and results and finally, section 5 discusses the conclusions and future work of the proposed system. ## related works the wide fashion domain requires studies in the research area to assist the shopping experience. for a long time, the limitation on computing resources was a drawback for companies to apply deep learning algorithms on their platform. fortunately, now technology allows processing a vast amount of data in a short time. the machine learning field is impacting the fashion industry in several ways including apparel designing, manufacturing process and, virtual merchandising foot_0 . several fashion retailers are using question answering systems @xcite , visual search, and automatic product tagging @xcite . nowadays, nlp as a machine learning technology improves the field of personalization by extracting the product characteristics such as attributes, reviews, feedback, and other information foot_1 . recently, the fashion industry is developing outfit recommenders by using machine learning techniques to decrease the time for exploring and combining products along with trended styles. consequently, in @xcite they addressed the task of outfit recommendation by suggesting a bag of lower body clothes to a selected top. that is to say, a neural multi-task learning framework, called neural outfit recommendation (nor) has been introduced consisting of two: a convolutional neural network with an attention mechanism to mine visual attributes of products and then a recurrent neural network to translate the visual information into text format. as a result, the mutual information among products is discovered and the system can recommend outfits using these features. in @xcite they used k-nearest neighbour algorithm to detect the nearest neighbor or a cluster based on the k value. there is also a considerable amount of work on recommending different types of categories such as dresses, backbags, heels and handbags along with rating top k products to display @xcite . one implemented solution uses a text mining approach to improve ranked based recommender systems. also, in @xcite a combination of k-means clustering @xcite along with k-nearest neighbor is implemented on the movielens dataset (harper and @xcite to achieve an improving result. in their proposed technique, the recommender system predicts the user's preference for a movie based on different parameters which also can be applied to product categories in the fashion industry. ## proposed methods this section describes the two proposed methods of our recommender system. the first subsection explains the survey content-based recommender system architecture within an example. the second subsection provides the enriched collaborative-based recommender system with the stylist, including architecture and examples.