Ntt Docomo, Inc.

Midori Onogi

Shoji Kurakake

Yusuke Fukazawa

Takefumi Naganuma

Hidetoshi Ueno

Mirai Hara

Masashi Onogi

A content evaluation apparatus and a content evaluation method that can distribute contents that a user potentially needs without any omissions. A history class separation unit generates a separation plane for separating in a separation plane a characteristic vector of a selected content and characteristic vectors of non-selected contents stored in a browsing history table. A user characteristic vector calculation unit generates a user characteristic vector based on an orthogonal vector perpendicular to the separation plane thus generated and stores the user characteristic vector in a user characteristic vector management table. Subsequently, when a recommendation request receiving unit receives a recommendation request, a content evaluation unit evaluates the contents based on the stored user characteristic vector.

Recommendation information evaluation apparatus using support vector machine with relative dissatisfactory feature vectors and satisfactory feature vectors

A recommendation information generation apparatus and a recommendation information generation method that can select a similar user appropriately even if the amount of history information is small. In an information distribution server, a user characteristic vector calculation unit calculates a user characteristic vector of each of users, and a similar user calculation unit calculates the degree of similarity between the users based on the calculated user characteristic vector of each of the users. The similar user calculation unit then selects a similar user who is similar to a first user based on the calculated degree of similarity. A relevance evaluation unit generates recommendation information for the first user based on the characteristic vector of the selected similar user.

Recommendation information generation apparatus and recommendation information generation method

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Yokohama, Japan

Midori Onogi