Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering with temporal dynamics
Communications of the ACM
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Improving a hybrid literary book recommendation system through author ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Time-aware topic recommendation based on micro-blogs
Proceedings of the 21st ACM international conference on Information and knowledge management
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Libraries have large growing book collections. Library users have difficulty in browsing the whole collection when choosing new books to read, particularly when looking for books without a defined goal. In this case, recommendation systems come in hand and play an important role in improving library usability. Recommendations are based on ratings and the quality of recommendations depends on the quality of the ratings. In this paper, we adapted an item-based collaborative filtering algorithm by incorporating temporal information. We, then, analyze the influence of the rating age in final predictions. Our findings suggest that prediction quality improve when some conditions are met. Moreover, user tastes are more correlated with recent rated books than old ones.