recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. user similarity measurement plays an important role in collaborative filtering based recommender systems. in order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...

Online Social Network (OSN) is an online social platform that enables people to exchange information, get in touch with family members or friends, and also helps as a marketing tool. However, OSN suffers from various security and privacy issues. Trust, fundamentally, is made up of security with hard trust (cryptographic mechanism) and soft trust (recommender system); user's trustworthiness for ...

Extensive literature review revealed that, different recommender systems for E-learning were developed. A preliminary version of the development was undertaken and evaluated in an experiment during an introduction psychology course in an open university. The activities were integrated in to a model which operates on a network. No curriculum structure was assigned and the users were allowed to u...

The literature on intelligent or adaptive tutoring systems generally has a focus on how to determine what resources to present to students as they make their way through a course of study. The idea of multi-faceted student modeling is that a variety of measures, both academic and nonacademic, might be represented in student models in service of a broader educational context. This paper contains...

Collaborative filtering Recommender system plays a very demanding and significance role in this era of internet information and of course e commerce age. Collaborative filtering predicts user preferences from past user behaviour or user-item relationships. Though it has many advantages it also has some limitations such as sparsity, scalability, accuracy, cold start problem etc. In this paper we...

The need to support users of the Internet with the selection of information is becoming more important. Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research questions regarding efficiency and effectiveness de...

Recommender systems help web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected. Active learning for recommender systems has been proposed in the past, to acquire preference info...

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 ...

The implementation of recommender systems in electronic procurement processes for service packages, consisting of productand service components requires a consideration of strategic, tactical and operational procurement as well as information and communication technologies in value networks. Increasingly, the design of recommender systems for procurement processes in value networks is of scient...