Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Adaptive collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Ranking with ordered weighted pairwise classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Exponential family sparse coding with applications to self-taught learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Localized factor models for multi-context recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
User oriented tweet ranking: a filtering approach to microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
The Joint Inference of Topic Diffusion and Evolution in Social Communities
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Retweet Modeling Using Conditional Random Fields
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Proceedings of the fifth ACM international conference on Web search and data mining
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Prediction in a microblog hybrid network using bonacich potential
Proceedings of the 7th ACM international conference on Web search and data mining
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
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Users of popular services like Twitter and Facebook are often simultaneously overwhelmed with the amount of information delivered via their social connections and miss out on much content that they might have liked to see, even though it was distributed outside of their social circle. Both issues serve as difficulties to the users and drawbacks to the services. Social media service providers can benefit from understanding user interests and how they interact with the service, potentially predicting their behaviors in the future. In this paper, we address the problem of simultaneously predicting user decisions and modeling users' interests in social media by analyzing rich information gathered from Twitter. The task differs from conventional recommender systems as the cold-start problem is ubiquitous, and rich features, including textual content, need to be considered. We build predictive models for user decisions in Twitter by proposing Co-Factorization Machines (CoFM), an extension of a state-of-the-art recommendation model, to handle multiple aspects of the dataset at the same time. Additionally, we discuss and compare ranking-based loss functions in the context of recommender systems, providing the first view of how they vary from each other and perform in real tasks. We explore an extensive set of features and conduct experiments on a real-world dataset, concluding that CoFM with ranking-based loss functions is superior to state-of-the-art methods and yields interpretable latent factors.