Abstract Conversational recommender systems aim to interactively support online users in their information search and decision-making processes an intuitive way. With the latest advances voice-controlled devices, natural language processing, AI general, such received increased attention recent years. Technically, conversational recommenders are usually complex multi-component applications often...

The tremendous growth of data in recent years poses some key challenges for recommender systems. Theses keys are related with producing high quality recommendations and fast performing the composition recommended items. In this paper, we propose social clustering-based similar user index to not only improve the prediction of recommendations, but also compose personalized recommendations in fast...

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alte...

According to the feedback information from a user in the result sets of initial or previous queries, we present in this paper a framework for processing recommender top-N queries in relational databases. Based on the techniques and ranking strategies of keyword search, this framework returns top-N results for an initial query given by the user. As soon as he or she selects some of the top-N res...

Background: Nutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their cha...

With the increasing popularity of collaborative tagging systems, services that assist the user in the task of tagging, such as tag recommenders, are more and more required. Being the scenario similar to traditional recommender systems where nearest neighbor algorithms, better known as collaborative filtering, were extensively and successfully applied, the application of the same methods to the ...