Evolutionary Programming based Recommendation System for Online Shopping

Abstract-In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the clothes they like differ from clothes they do not like. Therefore, in the proposed system, human preference is measured from the way the human subjects look at the images of different clothes. In other words, the human preference can be measured by using the fixation count and the fixation length using an eye tracking system. Based on the level of human preference, the evolutionary programming suggests new clothes that close the human preference by operations such as selection and mutation. The proposed recommendation is tested with several human subjects and the experimental results are demonstrated

Apparel recommendation system evolution: an empirical review

Purpose: With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approach: This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords. Findings: This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors' research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/value: Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective

Sistema de recomendação de imagens baseado em atenção visual

Nowadays, the amount of users using e-commerce sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. Many e-commerce sites, differently from physical stores, can offer their users a wide range of products and services, and the users can find it very difficult to find products of your preference. Typically, your preference for a product can be influenced by the visual appearance of the product image. In this context, Image Recommendation Systems (IRS) have become indispensable to help users to find products that may possibly pleasant or be useful to them. Generally, IRS use past behavior of users (clicks, purchases, reviews, ratings, etc.) and/or attributes of the products to define the preferences of users. One of the main challenges faced by IRS is the need of the user to provide some information about his / her preferences on products in order to get further recommendations from the system. Unfortunately, users are not always willing to provide such information explicitly. So, in order to cope with this challenge, methods for obtaining user’s implicit feedback are desirable. In this work, the author propose an investigation to discover to which extent information concerning user visual attention can help improve the rating prediction hence produce more accurate IRS. This work proposes to develop two new methods, a method based on Collaborative Filtering (CF) which combines ratings and data visual attention to represent the past behavior of users, and another method based on the content of the items, which combines textual attributes, visual features and visual attention data to compose the profile of the items. The proposed methods were evaluated in a painting dataset and a clothing dataset. The experimental results show significant improvements in rating prediction and precision in recommendation when compared to the state-of-the-art. It is worth mentioning that the proposed techniques are flexible and can be applied in other scenarios that exploits the visual attention of the recommended items.Conselho Nacional de Desenvolvimento Científico e TecnológicoTese (Doutorado)Hoje em dia, a quantidade de usuários que utilizam sites de comércio eletrônico para realizar compras está aumentando muito, principalmente devido à facilidade e rapidez. Muitos sites de comércio eletrônico, diferentemente das lojas físicas, disponibilizam aos seus usuários uma grande variedade de produtos e serviços, e os usuários podem ter muita dificuldade em encontrar produtos de sua preferência. Normalmente, a preferência por um produto pode ser influenciada pela aparência visual da imagem do produto. Neste contexto, os Sistemas de Recomendação de produtos que estão associados a Imagens (SRI) tornaram-se indispensáveis para ajudar os usuários a encontrar produtos que podem ser, eventualmente, agradáveis ou úteis para eles. Geralmente, os SRI usam o comportamento passado dos usuários (cliques, compras, críticas, avaliações, etc.) e/ou atributos de produtos para definirem as preferências dos usuários. Um dos principais desafios enfrentados em SRI é a necessidade de o usuário fornecer algumas informações sobre suas preferências sobre os produtos, a fim de obter novas recomendações do sistema. Infelizmente, os usuários nem sempre estão dispostos a fornecer tais informações de forma explícita. Assim, a fim de lidar com esse desafio, os métodos para obtenção de informações de forma implícita do usuário são desejáveis. Neste trabalho, propõe-se investigar em que medida informações sobre atenção visual do usuário podem ajudar a melhorar a predição de avaliação e consequentemente produzir SRI mais precisos. É também objetivo deste trabalho o desenvolvimento de dois novos métodos, um método baseado em Filtragem Colaborativa (FC) que combina avaliações e dados de atenção visual para representar o comportamento passado dos usuários, e outro método baseado no conteúdo dos itens, que combina atributos textuais, características visuais e dados de atenção visual para compor o perfil dos itens. Os métodos propostos foram avaliados em uma base de imagens de pinturas e uma base de imagens de roupas. Os resultados experimentais mostram que os métodos propostos neste trabalho possuem ganhos significativos em predição de avaliação e precisão na recomendação quando comparados ao estado-da-arte. Vale ressaltar que as técnicas propostas são flexíveis, podendo ser utilizadas em outros cenários que exploram a atenção visual dos itens recomendados