Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Accuracy <s> The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Accuracy <s> The paper describes a model for recommendations in learning scenarios which has been designed from empirical findings following usability and accessibility criteria. This model supports course designers in describing recommendations and presents additional information to the user to explain why the recommendation has been provided. A prototype of a recommender system based on this model has been integrated in an open source standard-based learning management system. The main goal of the recommender is to improve the learning efficiency. Examples of recommendations defined with this model are provided. Moreover, a users' experience is reported. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Accuracy <s> As it is difficult for learners to discover and obtain the most appropriate resources from massive education resources according to traditional keyword searching method, the context-aware based resource recommendation service becomes a significant part of pervasive learning environments. At present, recommendation mechanisms are widely used in e-commerce field, where content-based or collaborative-based filter strategies are usually considered separately. However, in these existing recommendation mechanisms, the dynamic interests and preference of learners, the access pattern and the other attributes of pervasive learning environments (such as multi-modes connecting and resources distribution) are always neglected. Thus, these mechanisms can not effectively reflect learners' actual preference and can not adapt to pervasive learning environments perfectly. To address these problems, a context-aware resource recommendation model and relevant recommendation algorithm for pervasive learning environments are proposed. Therein, with taking into account the relevant contextual information, the calculation of relevant degree between learners and resources can be divided into two main parts: logic-based RRD (resource relevant degree) and situation-based RRD. In the first part, content-based and collaborative-based recommendation mechanisms are combined together, where the individual preference tree (IPT) is introduced to take into account the multi-dimensional attributes of resources, learners' rating matrix and the energy of access preference. Meanwhile, the learners' historical sequential patterns of resource accessing are also considered to further improve the accuracy of recommendation. In the second part, in order to enhance the validation of recommendation, the connecting type relevance and time satisfaction degree are calculated according to other relevant contexts. Then, the candidate resources can be filtered and sorted via combining these two parts to generate (Top-N) recommendation results. The simulations show that our newly proposed method outperforms other state of-the-art algorithms on traditional and newly presented metrics and it may also be more suitable for pervasive learning environments. Finally, a prototype system is implemented based on SEU-ESP to demonstrate the relevant recommendation process further. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Accuracy <s> This paper discusses the 3A recommender system that targets CSCL (computer-supported collaborative learning) and CSCW (computer-supported collaborative work) environments. The proposed system models user interactions in a heterogeneous graph. Then, it applies a personalized, contextual, and multi-relational ranking algorithm to simultaneously rank actors, activity spaces, and assets. The results of an empirical evaluation carried out on an Epinions dataset indicate that the proposed recommendation approach exploiting the trust and authorship networks performs better than user-based collaborative filtering in terms of recall <s> BIB004
Accuracy of recommendations has been evaluated for five systems. Khribi et al. BIB001 measured the accuracy of the approach with precision and recall measures. Results indicate that precision increases when more recently visited pages are considered to capture context and decreases when the number of recommended resources increases. Whereas the approach is promising, the number of visited pages considered is limited to four in the evaluation experiment and seems a bit arbitrary. The accuracy of PL-CR 2 BIB003 was evaluated in a simulation experiment. Results indicate that the method outperforms traditional contentbased and collaborative filtering recommendation techniques with a significant increase of precision. Evaluation results of the 3A recommendation algorithm BIB004 indicate that the incorporation of relations outperforms standard collaborative filtering in terms of recall. In several experiments, Santos et al. BIB002 ,  gathered feedback on the accuracy of different types of recommendations. Among others, they conclude that recommendations based on learning styles are most appreciated, whereas collaboration recommendations were considered less relevant. Such evaluations are interesting, as they provide some insight into the relevancy of different contextual data in the recommendation process.