Fab: content-based, collaborative recommendation
Communications of the ACM
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
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
Yoda: An Accurate and Scalable Web-Based Recommendation System
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback
Distributed and Parallel Databases
The Journal of Machine Learning Research
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Open user profiles for adaptive news systems: help or harm?
Proceedings of the 16th international conference on World Wide Web
A hybrid approach for movie recommendation
Multimedia Tools and Applications
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Computers in Human Behavior
Information Sciences: an International Journal
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Hybrid algorithms for recommending new items
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
A movie recommendation algorithm based on genre correlations
Expert Systems with Applications: An International Journal
Lexicon-based Comments-oriented News Sentiment Analyzer system
Expert Systems with Applications: An International Journal
Implicit feedback techniques on recommender systems applied to electronic books
Computers in Human Behavior
IEEE Transactions on Audio, Speech, and Language Processing
An implementation and evaluation of recommender systems for traveling abroad
Expert Systems with Applications: An International Journal
PENETRATE: Personalized news recommendation using ensemble hierarchical clustering
Expert Systems with Applications: An International Journal
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.