Data mining also known as Knowledge Discovery in Database is the process of discovering new pattern from large data set. E-learning is the electronically learning & teaching process. Course Recommender System allows us to study the behavior of student regarding the courses. In Course Recommender System in E-learning, we collect the data regarding the student enrollments for a specific set of da...

Data mining also known as Knowledge Discovery in Database is the process of discovering new pattern from large data set. E-learning is the electronically learning & teaching process. Course Recommender System allows us to study the behavior of student regarding the courses. In Course Recommender System in E-learning, we collect the data regarding the student enrollments for a specific set of da...

The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of us...

Many organizations are implementing recommender systems with the expectation to influence users’ actions. However, research has shown that poorly designed recommender systems may be counterproductive. For instance, if a recommender system provides too many recommendations, users cannot focus on relevant recommendations anymore. Therefore, recommender systems need to be balanced and adjusted to ...

Research on recommender systems is a challenging task, as is building and operating such systems. Major challenges include non-reproducible research results, dealing with noisy data, and answering many questions such as how many recommendations to display, how often, and, of course, how to generate recommendations most effectively. In the past six years, we built three research-article recommen...

In general, the study of recommender systems emphasizes the efficiency of techniques to provide accurate recommendations rather than factors influencing users’ acceptance of the system; however, accuracy alone cannot account for users’ satisfying experience. Bearing in mind this gap in the research, we apply the technology acceptance model (TAM) to evaluate user acceptance of a recommender syst...

Withtherapiddevelopmentofmobileinternet,itisdifficulttoobtainhigh-qualityrecommendationin suchacomplicatedmobileenvironment,justdependingontraditionaluser-itembinaryinformation. Howtousemultiplecontextstogeneratesatisfyingrecommendationhasbeenahottopicinsomefields likee-commerce,tourismandnews.Contextawarerecommendersystem(CARS)importsconte...

Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of mul...

This study investigates how consumers assess the quality o f two types o f recommender systems , co llaborative filtering and content -based, in the content of e-commerce by using a modified Unified Theory o f Acceptance and Use o f Techno logy (UTAUT) model. Specifically, the under-investigated concept o f trust in techno log ical artifacts is adap ted to a modified UTAUT model. Additionally, ...

Personalization in learning management systems (LMS) occurs when such systems tailor the learning experience of learners such that it fits to their profiles, which helps in increasing their performance within the course and the quality of learning. A learner’s profile can, for example, consist of his/her learning styles, goals, existing knowledge, ability and interests. Generally, traditional L...