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

Research output: Contribution to conferencePaperResearchpeer-review

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 languageEnglish
Publication statusPublished - 20 Aug 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Conference

Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period22/10/1826/10/18

Fingerprint

Collaborative filtering
Recommender systems
Search engines

Keywords

  • cs.IR

Cite this

Lacic, E., Kowald, D., & Lex, E. (2018). Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.

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

2018. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.

Research output: Contribution to conferencePaperResearchpeer-review

Lacic, E, Kowald, D & Lex, E 2018, 'Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations' Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22/10/18 - 26/10/18, .
Lacic E, Kowald D, Lex E. Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. 2018. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.
Lacic, Emanuel ; Kowald, Dominik ; Lex, Elisabeth. / Neighborhood Troubles : On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. Paper presented at 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy.
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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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