A Retrieval Method Adaptively Reducing User's Subjective Impression Gap
A Retrieval Method Adaptively Reducing User's Subjective Impression Gap
复制标题
一种自适应缩小用户主观印象差距的检索方法
DOI：
10.1007/s11042-010-0690-0
复制
发表时间：
2011
影响因子：
3.6
通讯作者：
S. Tsuruta
中科院分区：
文献类型：
--
作者：
Y. Sakurai;K. Takada;R. Knauf;S. Tsuruta
作为在互联网上搜索/检索诸如图片、音乐、香水和服装等对象的一种方法，由于文本关键字不足以找到用户想要的对象，因此灵敏度向量或感性向量是有用的。敏感度向量是一组值。每个值表示一个敏感词或感性词所代表的感觉或印象的程度。然而，由于用户的主观敏感程度(印象、图像和感觉)与数据库中相应的值之间存在差距。此外，这样的方法不足以检索用户想要的东西。本文提出了一种检索方法，通过利用用户的检索历史和模糊建模来估计主观标准偏差(我们称之为“SCD”)，从而自动动态地缩小这种差距。此外，该方法还可以避免填写所需问卷等传统方法给用户带来的负担。这种方法还可以反映用户偏好的动态变化，而这是使用问卷调查无法完成的。为了进行评估，通过建立和使用香水检索系统进行了一项实验。通过观察偏差度的变化，说明该方法是有效的。在实验中，只要调整好学习率，机器就可以学习用户的主观标准偏差以及用户偏好等因素引起的用户主观标准偏差及其动态变化。
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.