Fab: content-based, collaborative recommendation
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A Hybrid Recommender System Combining Collaborative Filtering with Neural Network
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
An approach for combining content-based and collaborative filters
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
Relevance feedback models for recommendation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Hybrid web recommender systems
The adaptive web
An Improved Hybrid Recommender System by Combining Predictions
WAINA '11 Proceedings of the 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications
Authorship attribution with latent Dirichlet allocation
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
The impact of author ranking in a library catalogue
Proceedings of the 4th ACM workshop on Online books, complementary social media and crowdsourcing
Improving a hybrid literary book recommendation system through author ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Booksonline'12: 5th workshop on online books, complementary social media and their impact
Proceedings of the 21st ACM international conference on Information and knowledge management
Book recommender prototype based on author's writing style
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Reading is an important activity for individuals. Content-based recommendation systems are, typically, used to recommend scientific papers or news, where search is driven by topic. Literary reading or reading for leisure differs from scientific reading, because users search books not only for their topic but also by author or writing style. Choosing a new book to read can be tricky and recommendation systems can make it easy by selecting books that the user will like. In this paper we study recommendation through writing style and the influence of negative examples in user preferences. Our experiments were conducted in a hybrid set-up that combines a collaborative filtering algorithm with stylometric relevance feedback. Using the LitRec data set, we demonstrate that writing style influences book selection; that book content, characterized with writing style, can be used to improve collaborative filtering results; and that negative examples do not improve final predictions.