Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Retrieval of paintings by specifying impression words
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fuzzy Control and Modeling: Analytical Foundations and Applications
Fuzzy Control and Modeling: Analytical Foundations and Applications
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
Mining User preference using Spy voting for search engine personalization
ACM Transactions on Internet Technology (TOIT)
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Programming collective intelligence
Programming collective intelligence
Evaluation of Human Visual Impressions in Gray Scale Textures Using Morphological Manipulation
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Personalized movie recommendation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A method for constructing a movie-selection support system based on Kansei engineering
Proceedings of the 2007 conference on Human interface: Part I
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
User preference modeling based on interest and impressions for news portal site systems
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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As an approach to search/retrieve such objects as pictures, music, perfumes and apparels on the Internet, sensitivity-vectors or kansei-vectors are useful since textual keywords are not sufficient to find objects that users want. The sensitivity-vector is an array of values. Each value indicates a degree of feeling or impression represented by a sensitivity word or kansei word. However, due to the gap between user's subjective sensitivity (impression, image and feeling) degree and the corresponding value in the database. Also, such an approach is not enough to retrieve what users want. This paper proposes a retrieval method to automatically and dynamically reduce such gaps by estimating a subjective criterion deviation (we call "SCD") using the user's retrieval history and fuzzy modeling. Additionally, the proposed method can avoid users' burden caused by conventional methods such as completing required questionnaires. This method can also reflect the dynamic change of user's preference which cannot be accomplished by using questionnaires. For the evaluation, an experiment was performed by building and using a perfume retrieval system. Through observing the transition of the deviation reduction degree, it was clarified that the proposed method is effective. In the experiment, the machine could learn users' subjective criteria deviation as well as its dynamic change caused by factors such as user's preference, if the learning rate is well adjusted.