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In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.

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