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

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

Ethical Discussions in the National Literature as a Form of Moral Education of the Students

The article is devoted to the problems of moral education of students through the ethical conversations at the seminars of the course "history of Tatar literature". On the example of several classical works, firstly, the educational potential of literature is shown, and secondly, the effectiveness of ethical conversations and the system of value-oriented situations is emphasized. Experimental work on the basis of ranking the life values of young people shows the degree of effectiveness of the combination of ethical conversations and value-oriented situations in the formation of moral personality