An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
Attack detection in time series for recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Proposing an ESL recommender teaching and learning system
Expert Systems with Applications: An International Journal
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Evaluation of recommender systems: A new approach
Expert Systems with Applications: An International Journal
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
TREPPS: A Trust-based Recommender System for Peer Production Services
Expert Systems with Applications: An International Journal
User credit-based collaborative filtering
Expert Systems with Applications: An International Journal
Gradual trust and distrust in recommender systems
Fuzzy Sets and Systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
Collaborative filtering recommender systems
The adaptive web
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Information Sciences: an International Journal
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
Information Sciences: an International Journal
Information Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
Incorporating reliability measurements into the predictions of a recommender system
Information Sciences: an International Journal
A balanced memory-based collaborative filtering similarity measure
International Journal of Intelligent Systems
An effective recommendation method for cold start new users using trust and distrust networks
Information Sciences: an International Journal
Mining user similarity based on routine activities
Information Sciences: an International Journal
Trees for explaining recommendations made through collaborative filtering
Information Sciences: an International Journal
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
Improving collaborative filtering-based recommender systems results using Pareto dominance
Information Sciences: an International Journal
Knowledge-Based Systems
Hierarchical graph maps for visualization of collaborative recommender systems
Journal of Information Science
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
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It seems reasonable to think that there may be some items and some users in a recommender system that could be highly significant in making recommendations. For instance, the recent and much-advertised Apple product may be regarded as more significant compared with an outdated MP3 device (which is still on sale). In this paper, we introduce a new method to improve the information used in collaborative filtering processes by weighting the ratings of the items according to their importance. We provide here a formalisation of the collaborative filtering process based on the concept of significance. In this way, the k-neighbours are calculated taking into account the ratings of the items, the significance of the items and the significance of each user for making recommendations to other users. This formalisation includes extensions of the concepts related to similarity measures and prediction/recommendation quality measures. We will show also the results obtained from a set of experiments using Movielens and Netflix. The results confirm the advantage of introducing the concept of significance in general recommender systems and especially in recommender systems in which it is easy to determine the relative importance of the items: for example, most widely sold products in e-commerce, most widely commented news items in web-news, most widely watched programs on TV, and the latest sports champions.