A retrieval method adaptively reducing user's subjective impression gap
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.
information retrieval kansei engineering user profile