Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
This work addresses efficiency and accuracy challenges in recommender systems for users and platforms, but it appears incremental as it builds on existing collaborative filtering methods with a pre-filtering optimization.
The paper tackles the problem of speeding up and enhancing collaborative filtering recommendations by proposing a user pre-filtering step to extract a smaller set of candidate neighbors with high entity overlap, which was evaluated on a Foursquare dataset and resulted in improved runtime performance and recommendation accuracy.
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.