Collaborative Filtering Recommender Systems predict user preferences for online
information, products or services by learning from past user-item relationships. A …

Context-aware Recommendations (CARS) have attracted a lot of attention recently because
of the impact of contextual information on user behaviors. Recent state-of-the-art methods …

As a vital process to the success of an organization, salary benchmarking aims at identifying
the right market rate for each job position. Traditional approaches for salary benchmarking …

This dissertation deals with adaptive learning technologies which aim to optimize
Technology Enhanced Learning (TEL) offerings to fit the individual learner's needs. Thereby …

With increasingly more complex information spaces on the Internet, recommender systems
have emerged as critical tools to alleviate information overload and to assist decision …

Data analytic and data mining services are widely used these days as a result of the ever-
expanding amount of data being produced from various applications. Additionally, the …

Nowadays, the large amount of existing data makes the selection of relevant information
difficult for the users. This way, recommendation systems were developed to assist users in …

Now a day's students are interested in taking admission in college which having excellent
educational history, good campus, placement, better facilities, university grant etc. But due to …

Mobile smart devices are becoming increasingly important within the on-line purchasing
cycle. Thus the requirement for mobile commerce systems to become truly context-aware …

Recommender Systems are becoming a necessary agent for a naive user in the information
bombardment arena of World Wide Web. In the last decade, World Wide Web emerged as …