  Massive open online courses (MOOC) describe platforms where users with
completely different backgrounds subscribe to various courses on offer. MOOC
forums and discussion boards offer learners a medium to communicate with each
other and maximize their learning outcomes. However, oftentimes learners are
hesitant to approach each other for different reasons (being shy, don't know
the right match, etc.). In this paper, we propose a reciprocal recommender
system which matches learners who are mutually interested in, and likely to
communicate with each other based on their profile attributes like age,
location, gender, qualification, interests, etc. We test our algorithm on data
sampled using the publicly available MITx-Harvardx dataset and demonstrate that
both attribute importance and reciprocity play an important role in forming the
final recommendation list of learners. Our approach provides promising results
for such a system to be implemented within an actual MOOC.
