Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text processing
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
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Decision Tree for Business Intelligence and Data Mining
Decision Tree for Business Intelligence and Data Mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Analysis of cold-start recommendations in IPTV systems
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
Time-evolution of IPTV recommender systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Towards a journalist-based news recommendation system: The Wesomender approach
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Despite recommender systems based on collaborative filtering typically outperform content-based systems in terms of recommendation quality, they suffer from the new item problem, i.e., they are not able to recommend items that have few or no ratings. This problem is particularly acute in TV applications, where the catalog of available items (e.g., TV programs) is very dynamic. On the contrary, content-based recommender systems are able to recommend both old and new items but the general quality of the recommendations in terms of relevance to the users is low. In this article we present two different approaches for building hybrid collaborative+content recommender systems, whose purpose is to produce relevant recommendations, while overcoming the new item issue. The approaches have been tested on two datasets: a version of the well--known Movielens dataset enriched with content meta--data, and an implicit dataset collected from 15'000 IPTV users over a period of six months.