Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations

Publikation: KonferenzbeitragPaperForschungBegutachtung

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.
Originalspracheenglisch
PublikationsstatusVeröffentlicht - 20 Aug 2018
Veranstaltung27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italien
Dauer: 22 Okt 201826 Okt 2018

Konferenz

Konferenz27th ACM International Conference on Information and Knowledge Management, CIKM 2018
LandItalien
OrtTorino
Zeitraum22/10/1826/10/18

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Collaborative filtering
Recommender systems
Search engines

Schlagwörter

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    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.

    Neighborhood Troubles : On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. / Lacic, Emanuel; Kowald, Dominik; Lex, Elisabeth.

    2018. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.

    Publikation: KonferenzbeitragPaperForschungBegutachtung

    Lacic E, Kowald D, Lex E. Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. 2018. Beitrag in 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italien.
    Lacic, Emanuel ; Kowald, Dominik ; Lex, Elisabeth. / 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.
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    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.",
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    AB - 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.

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