GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Collaborative Learning and Recommender Systems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Technical paper recommendation: a study in combining multiple information sources
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Supervised ordering by regression combined with Thurstone's model
Artificial Intelligence Review
A novel collaborative filtering approach for recommending ranked items
Expert Systems with Applications: An International Journal
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
Extracting temporal signatures for comprehending systems biology models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
Nantonac collaborative filtering: a model-based approach
Proceedings of the fourth ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
Achieving fully proportional representation is easy in practice
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
Learning community-based preferences via dirichlet process mixtures of Gaussian processes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies similar to the user preference. Traditionally, the degree of preference is represented by a scale, for example, one that ranges from one to five. This type of measuring technique is called the semantic differential (SD) method. Web adopted the ranking method, however, rather than the SD method, since the SD method is intrinsically not suited for representing individual preferences. In the ranking method, the preferences are represented by orders, which are sorted item sequences according to the users' preferences. We here propose some methods to recommed items based on these order responses, and carry out the comparison experiments of these methods.