Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on 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
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Proceedings of the sixth ACM international conference on Web search and data mining
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
This paper presents a personalized news recommendation system that combines effective ways of understanding new articles with novel ways of modelling evolving user interest profiles to deliver relevant news articles to a user. A news article is represented as a taxonomy of hierarchical abstractions that capture different semantic facets of the news story. A users interest profile is modelled as an evolving interest over these facets. Users interest in individual articles is determined using a novel SWL (select-watch-leave) interest modelling framework that leverages on a detailed analysis of his usage history. Initial performance comparisons with state-of-the art personalized ranking approaches[2] are promising.