GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Efficient processing of window queries in the pyramid data structure
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Proceedings of the fifth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Sindbad: a location-based social networking system
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Proceedings of the 15th International Conference on Extending Database Technology
The anatomy of Sindbad: a location-aware social networking system
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.