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.
|Number of pages
|CEUR Workshop Proceedings
|Published - 1 Jan 2019
|2018 Conference on Information and Knowledge Management Workshops - Torino, Italy
Duration: 22 Oct 2018 → 22 Oct 2018
ASJC Scopus subject areas
- Computer Science(all)