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

TL;DR

This paper explores user pre-filtering in collaborative filtering to improve recommendation speed and accuracy by leveraging Apache Solr for scalable neighbor selection, validated on Foursquare data.

Contribution

It introduces a scalable user pre-filtering method using search engine features to enhance collaborative filtering recommendations.

Findings

Improved runtime performance in recommendation system.

Increased recommendation accuracy with pre-filtering.

Effective integration of Apache Solr for scalable neighbor selection.

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.