During the last ten years numerous research instruments emerged for evaluating the service quality of Web-based information systems. B2C recommender systems are information systems that support users in selecting information, products, or services improving their decision making process. Unfortunately, the majority of these instruments do not acknowledge the full range of recommendation functio...

World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users‟ point of view. Consequently it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. W...

Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of mul...

With the retail electronic commerce being a major global shopping phenomenon, retailers need to develop additional tools to improve their sales. One such tool is a Recommender System through which the shopping page recommends products to the shoppers using their past Web shopping and product search behavior. While recommender systems are common, few studies exist regarding their usability and u...

In requirements engineering for recommender systems, software engineers must identify the data that drives the recommendations. This is a labor-intensive task, which is errorprone and expensive. One possible solution to this problem is the adoption of automatic recommender system development approach based on a general recommender framework. One step towards the creation of such a framework is ...

This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical cluste...

The Internet provides large varieties of content, which renders consumption difficult for users. However, recommender systems filter and personalize content according to individual preferences and deliver solutions that take the problem of information overload into account. Previous studies show different approaches to classify existing recommender technologies. Nevertheless, these do not yet i...

Proper evaluation of the user experience of recommender systems requires conducting user experiments. This chapter is a guideline for students and researchers aspiring to conduct user experiments with their recommender systems. It first covers the theory of user-centric evaluation of recommender systems, and gives an overview of recommender system aspects to evaluate. It then provides a detaile...

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain kn...

Many organizations are implementing recommender systems with the expectation to influence users’ actions. However, research has shown that poorly designed recommender systems may be counterproductive. For instance, if a recommender system provides too many recommendations, users cannot focus on relevant recommendations anymore. Therefore, recommender systems need to be balanced and adjusted to ...