Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian parameter estimation via variational methods
Statistics and Computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Interactive retrieval based on faceted feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing the life cycle of online news stories using social media reactions
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
FAST: forecast and analytics of social media and traffic
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Personalized article recommendation is important for news portals to improve user engagement. Existing work quantifies engagement primarily through click rates. We suggest that quality of recommendations may be improved by exploiting different types of "post-read" engagement signals like sharing, commenting, printing and e-mailing article links. Specifically, we propose a multi-faceted ranking problem for recommending articles, where each facet corresponds to a ranking task that seeks to maximize actions of a particular post-read type (e.g., ranking articles to maximize sharing actions). Our approach is to predict the probability that a user would take a post-read action on an article, so that articles can be ranked according to such probabilities. However, post-read actions are rare events --- enormous data sparsity makes the problem challenging. We meet the challenge by exploiting correlations across different post-read action types through a novel locally augmented tensor (LAT) model, so that the ranking performance of a particular action type can be improved by leveraging data from all other action types. Through extensive experiments, we show that our LAT model significantly outperforms a variety of state-of-the-art factor models, logistic regression and IR models.