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  • Autor
    • Lacic, Emanuel
    • Kowald, Dominik
    • Lex, Elisabeth
  • TitelNeighborhood Troubles
  • Zusatz z. TitelOn the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
  • Datei
  • Persistent Identifier
  • Erschienen inEntity Retrieval Workshop @ ACM CIKM 2018 Conference
  • Erscheinungsjahr2018
  • LicenceCC-BY
  • ZugriffsrechteCC-BY
  • Download Statistik468
  • Peer ReviewJa
  • 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.