Analyzing social relations for recommending academic conferences
Felix Beierle, Julian Tan, Kai Grunert · 2016
Recommender systems are used to filter through vast amounts of items and recommend those that potentially have the highest relevance for the user. Recently, research dealing with recommendations in academia increased. In this paper, we analyze to what extent social relations from existing data can be utilized to generate academic conference recommendations. We design and implement a social recommender system and show how, without the need for explicit ratings, viable recommendations can be made, while at the same time reducing the cost of kNN-neighborhood selection.