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.
|Publication status||Published - 20 Aug 2018|
|Event||27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy|
Duration: 22 Oct 2018 → 26 Oct 2018
|Conference||27th ACM International Conference on Information and Knowledge Management, CIKM 2018|
|Period||22/10/18 → 26/10/18|