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.
|Publikationsstatus||Veröffentlicht - 20 Aug 2018|
|Veranstaltung||27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italien|
Dauer: 22 Okt 2018 → 26 Okt 2018
|Konferenz||27th ACM International Conference on Information and Knowledge Management, CIKM 2018|
|Zeitraum||22/10/18 → 26/10/18|
Lacic, E., Kowald, D., & Lex, E. (2018). Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.