Academic events are growing in numbers worldwide annually for
researchers to discuss their work. The research on recommendation systems in
academic domains has high significance for researchers. The classical approach to the
recommender system uses content-based and collaborative filtering that tends to
produce poor results. The focus of the study is to determine the factors involving the
selection of academic events and create a user-based personalised recommender system
for academic events. A survey will be conducted to identify the factors affecting the
choice of events. The system will filter the results of the events using a matching
matrix by conducting a factor analysis and receiving input to find the most relevant
academic events from the database. The study's approach evaluates the result based on
the pre-processed data and the similarity measures between a similar user (Top-n) and
an active user for events with a higher probability of participation. The weighted
average of the neighbour’s ratings will be generated for the predictions of the events.
The study’s outcome will prove that the personalised recommendation system is better
than the classical approach in finding the most relevant events. The recommendation
system can be optimised in domains.
Keywords: Academic Event, Collaborative Filtering, Factor Analysis, Matching Matrix, Recommender System
