Recommendation of visual information by gaze-based implicit preference acquisition

  • Authors:

  • Atsuo Yoshitaka;Kouki Wakiyama;Tsukasa Hirashima

  • Affiliations:

  • Graduate School of Engineering, Hiroshima University, Hiroshima, Japan;Graduate School of Engineering, Hiroshima University, Hiroshima, Japan;Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

  • Venue:
  • MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
  • Year:
  • 2007

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Abstract

Collaborative filtering is one of the information filtering techniques, that recommends information referring to the evaluation of others' feedback, where their preferences are similar to the target user to assist. The user's preference is often specified explicitly, and it is often a burden or disturbance in concentrating on his/her primary activity. This issue affects the granularity, i.e., degree of detail of user's feedback, since it largely depends on the easiness of acquisition for the user's preference. In this paper we describe a method of information recommendation based on social filtering, where the preference of user is implicitly acquired by gaze detection. As an example of the application we implemented a system that recommends paintings to a person based on others' attention in appreciating paintings. The evaluation of preference is based not on individual objects (i.e., paintings) but on sub-regions in an object (e.g., a person, a building, an animal, and so on), that is detected by gaze point tracking. The strength of interest is measured as the duration of watching a sub-region in the painting, and each user's interest model is organized based on it. Experimental result showed the sub-region based information recommendation provides us better recommendation compared with object-based recommen-dation, and the proposed implicit preference acquisition method is comparable to explicit preference specification method.