Abstract-Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items' semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based MultiCriteria Collaborative Filtering algorithm that integrates the items' semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques. According to the experimental results, the proposed algorithm prove to be very effective in terms of dealing with both of the sparsity and new item problems and therefore produce more accurate recommendations when compared to standard itembased CF techniques.

Abstract:With the rapid increasing of learning objects (LOs) in a variety of media formats, it becomes quite difficult and complicated task for learners to find suitable LOs based on their needs and preferences. To support personalization, recommender systems can be used to assist learners in finding the appropriate LOs which will be needed for their learning. In this paper, we propose a framework of a semantic recommender system for e-learning in which it will assist learners to find and select the relevant LOs to their field of interest. The proposed framework utilizes the intra and extra semantic relationships between LOs and the learner's needs to provide personalized recommendations for learners. The semantic recommendation algorithm is based on the extension of the query keywords by using the semantic relations, concepts and reasoning means in the domain ontology. The proposed system can be used to reduce the time and effort involved in finding suitable LOs, and thus, improves the quality of learning.Key words: E-learning, learning object, personalization, recommender system, semantic web, semantic indexing system, ontology modeling, semantic query processing. IntroductionWith the rapid growth of Web-based learning applications, e-learning is becoming more and more popular than the traditional educational approaches. Learning management systems (LMSs) are typically employed in large-scale educational institutions to facilitate the delivery and organization of e-learning [1]. LMS can be defined as "the infrastructure that delivers and manages instructional content, identifies and assesses individual and organizational learning or training goals, tracks the progress towards meeting those goals, and collects and presents data for supervising the learning process of an organization as a whole" [2].In general, courses in LMSs consist of LOs. LO can be defined as a digital and reusable piece of content used to achieve a learning objective. LO can be a text document, an audio file, a video, a picture, or a complete website [3]. Commonly, LMSs are considered as one-size-fits-all systems as they deliver the same kind of course structure and LOs to each learner [1], [4]. However, each learner has different characteristics such as levels of expertise, learning styles, prior knowledge, cognitive abilities and interests, and therefore, a one-size-fits-all systems do not support most learners.Personalization is a promising way to deal with this problem by supporting each learner independently based on his/her characteristics. Personalization in LMSs occurs when such systems uniquely address a learner's needs and characteristics. This will help in improving the learner's satisfaction and in overall the quality of learning. To support personalization, recommender systems can be employed to overcome current limitations of LMSs in providing personalization through recommending suitable LOs to learners based on their individual needs and Salam Fraihat * , Qusai Shambour Software Engineering Department, Faculty of Information...

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Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field.