Discussion forums are used to support socio-collaborative learning processes among students in online courses. However, complex forum structures and lengthy discourse require that students spend their limited time searching and filtering through posts to find those that are relevant to them rather than spending that time engaged in other meaningful learning activities (i.e., discussion). Moreover, existing adaptive systems do not accommodate individual learner needs in these contexts. In this work, we propose a multi-relational graph-based recommendation approach that mines student interaction logs to address the above problems within discussionbased socio-collaborative online courses. To account for the social aspects of learning, our approach incorporates learner modeling, social network analysis, and natural language processing techniques; it offers tailored recommendations of forum posts for learners with different types of interaction behaviors. In our experiments with small online courses, our approach outperformed competitor approaches in terms of recommendation precision while meeting expectations with respect to diversity and novelty. The results illustrate the proposed algorithm's effectiveness in predicting student preferences, suggesting its potential to increase student participation in discussion-related learning activities. [For the full proceedings, see ED607784.]