Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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)
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th 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
Time-evolution of IPTV recommender systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
Hybrid algorithms for recommending new items
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Context-aware item-to-item recommendation within the factorization framework
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with social regularization for TV program recommendation
Knowledge-Based Systems
Probabilistic topic models for sequence data
Machine Learning
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 algorithms in cold-start situations, where the initial lack of ratings may affect the quality of the algorithms. The evaluation has been performed on the pay-per-view datasets collected by two IP-television providers over a period of several months. The analysis shows that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start problem. Moreover, the accuracy of SVD-based algorithms, when using few latent factors, decreases with the time-evolution of the dataset. On the contrary, the same algorithms used with a large-enough number of latent features increase their accuracy with time and may outperform the item-based algorithms.