Abstract
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.
Original language | English |
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Article number | 9 |
Number of pages | 5 |
Journal | CEUR Workshop Proceedings |
Volume | 2482 |
Publication status | Published - 1 Jan 2019 |
Event | 2018 Conference on Information and Knowledge Management Workshops - Torino, Italy Duration: 22 Oct 2018 → 22 Oct 2018 |
ASJC Scopus subject areas
- General Computer Science