Random sampling with a reservoir
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The nature of statistical learning theory
The nature of statistical learning theory
On-line learning and stochastic approximations
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Sparse Greedy Matrix Approximation for Machine Learning
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Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Fast online learning through offline initialization for time-sensitive recommendation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Combined regression and ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Towards Optimal Active Learning for Matrix Factorization in Recommender Systems
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Proceedings of the 21st ACM international conference on Information and knowledge management
CatStream: categorising tweets for user profiling and stream filtering
Proceedings of the 2013 international conference on Intelligent user interfaces
Towards real-time collaborative filtering for big fast data
Proceedings of the 22nd international conference on World Wide Web companion
Living analytics methods for the web observatory
Proceedings of the 22nd international conference on World Wide Web companion
Which app will you use next?: collaborative filtering with interactional context
Proceedings of the 7th ACM conference on Recommender systems
TeRec: a temporal recommender system over tweet stream
Proceedings of the VLDB Endowment
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
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The Social Web is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommendation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization - RMFX -, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the "476 million Twitter tweets" dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach.