Recommender systems support users in €nding relevant items in overloaded information spaces. Researchers and practitioners have proposed many di‚erent collaborative €ltering algorithms for different information scenarios, domains and contexts. One of the laŠer, are time-aware recommender methods that consider temporal dynamics in the users’ interests in certain items, topics, etc. While there i...

Recent advances in electronic media and computer networks have allowed the creation of large and distributed repositories of information. However, the immediate availability of extensive resources for use by broad classes of computer users gives rise to new challenges in everyday life. These challenges arise from the fact that users cannot exploit available resources effectively when the amount...

During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the gro...

Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...

User modelling and personalisation are the key aspects of recommender systems in terms of recommendation quality. While being very efficient and designed to work with huge amounts of data, present recommender systems often lack the facility of user integration when it comes to feedback and direct user modelling. We propose the introduction of dynamic logic programming – an extension of answer s...

With increasingly large friend networks, Facebook users may be losing sight of exactly with whom they are sharing content they post to Facebook. When Facebook released a new privacy interface in summer 2010 they simplified privacy controls; however, group-based permissions remain at the core of fine-grained privacy control. In order to use these fine-grained controls, users must be able to accu...

Several kinds of features of user navigation paths (e.g., subsets of the resources as nodes of the paths covered, subsequences of the sequences used for path description, path fragments constructed via combination of subsequences and wildcards) can be employed to build recommender systems designed for tasks as di erent as site personalization, cross-/up-selling, and navigation assistance. A voc...

Abstract Group formation is a complex task requiring computational support to succeed. In the literature, there has been considerable effort in development of algorithms for composing groups as well their evaluation. The most widely used approach Genetic Algorithm, as, it can handle numerous variables, generating optimal solutions according problem requirements. this study, novel genetic algori...

To date, recommender systems are a popular instrument to make personalized suggestions and provide information about items for users. There are many techniques that can be applied for personalization in recommender systems. All these techniques have complementary strengths and weaknesses. A hybrid recommender system combines two or more recommendation techniques to gain better system performanc...

The recommender system research community has become a well-identified and active research community built around the goal of understanding, building, and improving recommender systems. A recurring theme since the first workshop in Berkeley (March 1996) has been the development of substantial research infrastructure—typically publicly-accessible recommendation services—as a tool to conduct rese...