Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas. In this context, a centralized system is needed for effective monitoring and analysis of the medical records. A web-based patient diagnostic central platform store history predict possible disease based on current symptoms experienced by ensure faster accurate diagnosis. Early prediction can help users deter...

As the Web rapidly evolved into an immense repository of content, human users discovered that they could no longer effectively identify the content of most interest to them. Several approaches developed for improving our ability to find content. Syntactic search engines helped index and rapidly scan millions of pages for keywords, but we quickly learned that the amount of content with matching ...

Finding information from a large collection of resources is tedious and time-consuming process. Due to overload, searchers often need help assistance search find the information. Recommender system one innovative solutions problem related searching retrieval. It helps assist by recommending possible solution based on previous activities. These activities can be obtained web log, which requires ...

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly ...

Web recommender systems are used in different domain applications to predict customer preferences. They also used to assist the web users to satisfy their search in different products and services. Nowadays, a web system with effective and reliable recommendation techniques has become the target of the research in recent years. Number of methods has been proposed to the users for effective and ...

Matrix and tensor completion techniques have proven useful in many applications such as recommender systems, image/video restoration, and web search. We explore the idea of using external information in completing missing values in tensors. In this work, we present a framework that employs side information as kernel matrices for tensor factorization. We apply our framework to problems of recomm...

I. Cantador (), P. Castells Universidad Autónoma de Madrid 28049 Madrid, Spain e-mails: [email protected], [email protected] Abstract An increasingly important type of recommender systems comprises those that generate suggestions for groups rather than for individuals. In this chapter, we revise state of the art approaches on group formation, modelling and recommendation, and present ch...

This paper presents INTIMATE, a web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach...