An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
An Evaluation Methodology for Collaborative Recommender Systems
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Implementation of social features over regular IPTV stb
Proceedings of the seventh european conference on European interactive television conference
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
Real-time viewer feedback in the iTV production
Proceedings of the seventh european conference on European interactive television conference
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Do Metrics Make Recommender Algorithms?
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Analysis of cold-start recommendations in IPTV systems
Proceedings of the third ACM conference on Recommender systems
Hybrid algorithms for recommending new items
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Effectiveness of the data generated on different time in latent factor model
Proceedings of the 7th ACM conference on Recommender systems
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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In this paper we evaluate the performance of different collaborative filtering algorithms over time, where new users, new items, and new ratings are constantly added to the recommender dataset. The analysis has been performed on the datasets collected by two IPTV providers. Both datasets have been implicitly collected by analyzing the pay-per-view movies purchased by the users over a period of several months. The first result of the paper outlines that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start problem. The second result shows that the accuracy of SVD-based algorithms, when using few latent factors, decreases with the time-evolution of the dataset. On the contrary, SVD-based algorithms, when used with a large-enough number of latent features, increase their accuracy with time and may outperform the item-based algorithms if the dataset does not present a long-tail behavior.