A Brand-Aware Collaborative Filtering-Based Recommender System

Recommendig clothing products can be formidable: while making a purchase decision, of the many possible attributes, such as how fashionable or how popular the product is, customer’s aesthetic preference plays a significant role. As the online retail marketplace is growing rapidly, making the available product range extremely diverse, capturing customer preference is also becoming more and more challenging. In this article we propose an extended Collaborative Filtering algorithm, using additional side information in order to capture products’ styles, which are used to define a customer’s preference. Keywords: Recommender Systems, E-commerce, Collaborative Filterin

A Style-Aware Collaborative Filtering-Based Recommender System

Online shopping for clothing products is growing rapidly. In order to avoid choice overload and match consumers with the most suitable products, retailers use recommender systems. However, unlike other products, recommending clothes can be challenging. Most customers not only search a clothes by their popularity or price but also by style. We present a Collaborative Filtering recommender system based on the traditional Matrix Factorization which incorporates items’ contextual information in order to discover users’ aesthetic preferences. We apply a style-aware recommender model in a real-world dataset of Amazon for experimental evaluation, demonstrating that our algorithm outperforms the state-of-the-art CF-based recommender approach. Keywords: Recommender Systems, E-commerce, Collaborative Filterin

A Survey of e-Commerce Recommender Systems

Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems

Contextual Recommender Systems for Building and Construction Materials Business

วิศวกรรมศาสตรมหาบัณฑิต (วิศวกรรมคอมพิวเตอร์), 2565Nowadays, the recommendation system is one of the most important supported technologies to e-commerce that aims for recommending the products or services to be purchased, to increase sales. In this work, the focus on the recommendation system for the building materials business. Building materials business is a business that sales construction related materials and equipment, such as, structural goods, tools supplies, etc. For customers who come to buy products will builder professionally or customers who want to improve their homes. Products recommendation system in this business will recommend products that can be used in profession. Generally, system recommends products that are like the ones purchased but regardless of context or profession of the customer. In this paper, we propose a context awareness data modeling to specialize the recommendation system aiming for the building materials business.ทุนโครงการการอุดมศึกษาเพื่ออุตสาหกรรม (Higher Education for Industry: Hi-FI)ปัจจุบันระบบการแนะนำเป็นหนึ่งในเทคโนโลยีสำคัญที่สนับสนุนสำหรับอีคอมเมิร์ซ โดยมีจุดมุ่งหมายเพื่อแนะนำสินค้าหรือบริการที่ตรงตามความต้องการของผู้ซื้อ เพื่อเพิ่มยอดขายสำหรับธุรกิจ ในการวิจัยนี้เรามุ่งเน้นไปที่การพัฒนาระบบแนะนำสำหรับธุรกิจวัสดุก่อสร้าง ธุรกิจวัสดุก่อสร้างเป็นธุรกิจที่จำหน่ายวัสดุก่อสร้างและอุปกรณ์ที่เกี่ยวข้อง เช่น สินค้าโครงสร้าง อุปกรณ์เครื่องมือ และอื่นๆ สำหรับลูกค้าที่มาซื้อผลิตภัณฑ์จะเป็นช่างก่อสร้างหรือลูกค้าที่ต้องการปรับปรุงบ้าน ระบบแนะนำสินค้าในธุรกิจนี้จะแนะนำสินค้าที่สามารถนำไปใช้ในอาชีพได้ โดยทั่วไป ระบบจะแนะนำผลิตภัณฑ์ที่คล้ายกับที่ซื้อ แต่ไม่คำนึงถึงบริบทหรืออาชีพของลูกค้า โดยในงานวิจัยนี้ เราได้นำเสนอการสร้างแบบจำลองข้อมูลการรับรู้บริบท เพื่อที่จะพัฒนาระบบแนะนำสำหรับธุรกิจวัสดุก่อสร้า

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

Assessing and improving recommender systems to deal with user cold-start problem

Recommender systems are in our everyday life. The recommendation methods have as main purpose to predict preferences for new items based on userŠs past preferences. The research related to this topic seeks among other things to discuss user cold-start problem, which is the challenge of recommending to users with few or no preferences records. One way to address cold-start issues is to infer the missing data relying on side information. Side information of different types has been explored in researches. Some studies use social information combined with usersŠ preferences, others user click behavior, location-based information, userŠs visual perception, contextual information, etc. The typical approach is to use side information to build one prediction model for each cold user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most recommender systems falls a great deal. We, rather, propose that cold users are best served by models already built in system. In this thesis we propose 4 approaches to deal with user cold-start problem using existing models available for analysis in the recommender systems. We cover the follow aspects: o Embedding social information into traditional recommender systems: We investigate the role of several social metrics on pairwise preference recommendations and provide the Ąrst steps towards a general framework to incorporate social information in traditional approaches. o Improving recommendation with visual perception similarities: We extract networks connecting users with similar visual perception and use them to come up with prediction models that maximize the information gained from cold users. o Analyzing the beneĄts of general framework to incorporate networked information into recommender systems: Representing different types of side information as a user network, we investigated how to incorporate networked information into recommender systems to understand the beneĄts of it in the context of cold user recommendation. o Analyzing the impact of prediction model selection for cold users: The last proposal consider that without side information the system will recommend to cold users based on the switch of models already built in system. We evaluated the proposed approaches in terms of prediction quality and ranking quality in real-world datasets under different recommendation domains. The experiments showed that our approaches achieve better results than the comparison methods.Tese (Doutorado)Sistemas de recomendação fazem parte do nosso dia-a-dia. Os métodos usados nesses sistemas tem como objetivo principal predizer as preferências por novos itens baseado no perĄl do usuário. As pesquisas relacionadas a esse tópico procuram entre outras coisas tratar o problema do cold-start do usuário, que é o desaĄo de recomendar itens para usuários que possuem poucos ou nenhum registro de preferências no sistema. Uma forma de tratar o cold-start do usuário é buscar inferir as preferências dos usuários a partir de informações adicionais. Dessa forma, informações adicionais de diferentes tipos podem ser exploradas nas pesquisas. Alguns estudos usam informação social combinada com preferências dos usuários, outros se baseiam nos clicks ao navegar por sites Web, informação de localização geográĄca, percepção visual, informação de contexto, etc. A abordagem típica desses sistemas é usar informação adicional para construir um modelo de predição para cada usuário. Além desse processo ser mais complexo, para usuários full cold-start (sem preferências identiĄcadas pelo sistema) em particular, a maioria dos sistemas de recomendação apresentam um baixo desempenho. O trabalho aqui apresentado, por outro lado, propõe que novos usuários receberão recomendações mais acuradas de modelos de predição que já existem no sistema. Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start do usuário usando modelos existentes nos sistemas de recomendação. As abordagens apresentadas trataram os seguintes aspectos: o Inclusão de informação social em sistemas de recomendação tradicional: foram investigados os papéis de várias métricas sociais em um sistema de recomendação de preferências pairwise fornecendo subsidíos para a deĄnição de um framework geral para incluir informação social em abordagens tradicionais. o Uso de similaridade por percepção visual: usando a similaridade por percepção visual foram inferidas redes, conectando usuários similares, para serem usadas na seleção de modelos de predição para novos usuários. o Análise dos benefícios de um framework geral para incluir informação de redes de usuários em sistemas de recomendação: representando diferentes tipos de informação adicional como uma rede de usuários, foi investigado como as redes de usuários podem ser incluídas nos sistemas de recomendação de maneira a beneĄciar a recomendação para usuários cold-start. o Análise do impacto da seleção de modelos de predição para usuários cold-start: a última abordagem proposta considerou que sem a informação adicional o sistema poderia recomendar para novos usuários fazendo a troca entre os modelos já existentes no sistema e procurando aprender qual seria o mais adequado para a recomendação. As abordagens propostas foram avaliadas em termos da qualidade da predição e da qualidade do ranking em banco de dados reais e de diferentes domínios. Os resultados obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os métodos do estado da arte