“Formation of E-Commerce Recommendation (E-Shop)” (International Journal of Engineering and Computer Science, 2017)
Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features.Recommended systems are changing from novelties used by a few E-commerce sites, to serious business tools that are re-shaping the world of E-commerce. Many of the largest commerce Web sites are already using recommended systems to help their customers find products to purchase. A recommended system learns from a customer and recommends products that she will find most valuable from among the available products. In this paper we present an explanation of how recommended systems help E-commerce sites increase sales