Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Image Segmentation as Learning on Hypergraphs
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social reader: following social networks in the wilds of the blogosphere
WSM '09 Proceedings of the first SIGMM workshop on Social media
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Proceedings of the fourth ACM conference on Recommender systems
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Implicit news recommendation based on user interest models and multimodal content analysis
Proceedings of the 3rd international workshop on Automated information extraction in media production
Multiple hypergraph clustering of web images by mining Word2Image correlations
Journal of Computer Science and Technology
Hypergraph-based inductive learning for generating implicit key phrases
Proceedings of the 20th international conference companion on World wide web
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
LOGO: a long-short user interest integration in personalized news recommendation
Proceedings of the fifth ACM conference on Recommender systems
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Hybrid systems for personalized recommendations
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Personalized news recommendation: a review and an experimental investigation
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Personalized news recommendation based on implicit feedback
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
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Personalized news recommender systems have gained increasing attention in recent years. Within a news reading community, the implicit correlations among news readers, news articles, topics and named entities, e.g., what types of named entities in articles are preferred by users, and why users like the articles, could be valuable for building an effective news recommender. In this paper, we propose a novel news personalization framework by mining such correlations. We use hypergraph to model various high-order relations among different objects in news data, and formulate news recommendation as a ranking problem on fine-grained hypergraphs. In addition, by transductive inference, our proposed algorithm is capable of effectively handling the so-called cold-start problem. Extensive experiments on a data set collected from various news websites have demonstrated the effectiveness of our proposed algorithm.