Recommendation systems support users in finding items of interest. In this chapter, we introduce the basic approaches of collaborative filtering, contentbased filtering, and knowledge-based recommendation. We first discuss principles of the underlying algorithms based on a running example. Thereafter, we provide an overview of hybrid recommendation approaches which combine basic variants. We co...

In an e-commerce environment, personalization has taken on an important role in improving service levels, and fostering customer loyalty. In addition, the recommendation systems techniques that support many personalization systems are capable of customizing the recommendation of products and the display of advertisements to the individual level. This chapter provides a review of the major recom...

with the rapid expansion of the information on the internet, recommender systems play an important role in terms of trade and research. recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. ...

The latest advances in technology and the improvement of decision processes with learning methods based on artificial intelligence have put word "smart" ahead all systems that make human life easier. Based intelligent transportation systems, it is aimed to reduce damage country's economy environment while providing technology-based faster, safer, more accessible, sustainable efficient transport...

Recommendation systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommendation systems, content-based recommendation systems and a few hybrid systems. We propose a semantic framework to overcome common limitations of current systems. We present a system whose representations of items and user-profiles are based on conce...

Item-based collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm, which is widely used in many recommendation systems. But there are some drawbacks when used in large e-business systems. The existing traditional algorithms can’t perform well when the item space changes; on the other side, the performance of the recommendation system will go dow...

Modeling ranked data is an essential component in a number of important applications including recommendation systems and websearch. In many cases, judges omit preference among unobserved items and between unobserved and observed items. This case of analyzing incomplete rankings is very important from a practical perspective and yet has not been fully studied due to considerable computational d...

The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources in order to describe and organize them. A tag cloud provides rough impression of relative importance of each tag within the overall cloud in order to facilitate browsing among numerous tags and resources. The size of its vocabulary may be huge, moreover, it is incomplete and inconsistent. Thus, the goal o...

Effective personalization can help firms reduce their customers’ search costs and enhance customer loyalty. The personalization process consists of two important activities: learning and matching. Learning involves collecting data from a customer’s interactions with the firm and then making inferences from the data about the customer’s profile. Matching requires identifying which products to re...