  In this paper, we present work-in-progress on applying user pre-filtering to
speed up and enhance recommendations based on Collaborative Filtering. We
propose to pre-filter users in order to extract a smaller set of candidate
neighbors, who exhibit a high number of overlapping entities and to compute the
final user similarities based on this set. To realize this, we exploit features
of the high-performance search engine Apache Solr and integrate them into a
scalable recommender system. We have evaluated our approach on a dataset
gathered from Foursquare and our evaluation results suggest that our proposed
user pre-filtering step can help to achieve both a better runtime performance
as well as an increase in overall recommendation accuracy.
