GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
The Art of Computer Programming, Volume 4, Fascicle 3: Generating All Combinations and Partitions
The Art of Computer Programming, Volume 4, Fascicle 3: Generating All Combinations and Partitions
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Progressive and selective merge: computing top-k with ad-hoc ranking functions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Top-k query evaluation with probabilistic guarantees
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
ARCube: supporting ranking aggregate queries in partially materialized data cubes
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Introduction to recommender systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Tutorial on recent progress in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient processing of top-k join queries by attribute domain refinement
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
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Recommender systems help users find their items of interest from large data collections with little effort. Collaborative filtering (CF) is one of the most popular approaches for making recommendations. While significant work has been done on improving accuracy of CF methods, some of the most popular CF approaches are limited in terms of scalability and efficiency. The size of data in modern recommender systems is growing rapidly in terms of both new users and items and new ratings. Item-based recommendation is one of the CF approaches used widely in practice. It computes and uses an item-item similarity matrix in order to predict unknown ratings. Previous works on item-based CF method confirm its usefulness in providing high quality top-k results. In this paper, we design a scalable algorithm for top-k recommendations using this method. We achieve this by probabilistic modeling of the similarity matrix. A unique challenge here is that the ratings that are aggregated to produce the aggregate predicted score for a user should be obtained from different lists for different candidate items and the aggregate function is non-monotone. We propose a layered architecture for CF systems that facilitates computation of the most relevant items for a given user. We design efficient top-k algorithms and data structures in order to achieve high scalability. Our algorithm is based on abstracting the key computation of a CF algorithm in terms of two operations -- probe and explore. The algorithm uses a cost-based optimization whereby we express the overall cost as a function of a similarity threshold and determine its optimal value for minimizing the cost. We empirically evaluate our theoretical results on a large real world dataset. Our experiments show our exact top-k algorithm achieves better scalability compared to solid baseline algorithms.