Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Time-evolution of IPTV recommender systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
Fast als-based matrix factorization for explicit and implicit feedback datasets
Proceedings of the fourth ACM conference on Recommender systems
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Overlay management for fully distributed user-based collaborative filtering
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical study of matrix factorization methods for collaborative filtering
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Incremental methods in collaborative filtering for ordinal data
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Bayesian matrix co-factorization: variational algorithm and Cramér-Rao bound
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Distributed rating prediction in user generated content streams
Proceedings of the fifth ACM conference on Recommender systems
Applications of the conjugate gradient method for implicit feedback collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
Improving neighborhood based Collaborative Filtering via integrated folksonomy information
Pattern Recognition Letters
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
Property-based collaborative filtering for health-aware recommender systems
Expert Systems with Applications: An International Journal
Enhancing matrix factorization through initialization for implicit feedback databases
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Collaborative filtering via group-structured dictionary learning
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
The impact of recommender systems on item-, user-, and rating-diversity
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Ordinal incremental data in collaborative filtering
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
A hierarchical model for ordinal matrix factorization
Statistics and Computing
iMapReduce: A Distributed Computing Framework for Iterative Computation
Journal of Grid Computing
ACM Transactions on Interactive Intelligent Systems (TiiS)
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A parallel matrix factorization based recommender by alternating stochastic gradient decent
Engineering Applications of Artificial Intelligence
Scalable collaborative filtering using incremental update and local link prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Applying the learning rate adaptation to the matrix factorization based collaborative filtering
Knowledge-Based Systems
Knowledge-Based Systems
Scaling matrix factorization for recommendation with randomness
Proceedings of the 22nd international conference on World Wide Web companion
Scaling factorization machines to relational data
Proceedings of the VLDB Endowment
Scalable multimedia content analysis on parallel platforms using python
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient distributed monitoring with active Collaborative Prediction
Future Generation Computer Systems
Boosting the K-Nearest-Neighborhood based incremental collaborative filtering
Knowledge-Based Systems
Hierarchical Bayesian matrix factorization with side information
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Location sharing privacy preference: analysis and personalized recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
DMFSGD: a decentralized matrix factorization algorithm for network distance prediction
IEEE/ACM Transactions on Networking (TON)
Identify the User's Information Need Using the Current Search Context
International Journal of Enterprise Information Systems
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The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.