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A Recommendation approach based on Correlation and Co-occurrence within social learning network

In the context of e-learning, social learning is viewed as an evolving educational practice, namely in social networks. It is extensively associated with new educational technologies and fosters collaborative learning between learners. To handle the various pedagogical resources, several recommendation systems were proposed, with considerable emphasis on interactions and social relationships, except that they did not raise a critical aspect, namely the underlying nature of the relationship between the learners' actions and recommendations. To support the current recommendation systems, we propose a recommendation system that can measure the influence of learners' actions on the calculated recommendations. We therefore seek to evaluate the connection and link between these actions, and thus to combine the two parameters: correlation and co-occurrence by estimating the similarity of occurrences on the one hand and the probability of two actions taking place on the other hand.