@article{oai:doshisha.repo.nii.ac.jp:00022453,
 author = {宮地, 正大 and Miyaji, Masahiro and 田中, 美里 and Tanaka, Misato and 山本, 詩子 and Yamamoto, Utako and Hiroyasu, Tomoyuki and 廣安, 知之 and 三木, 光範 and Miki, Mitsunori and 横内, 久猛 and Yokouchi, Hisatake},
 issue = {3},
 journal = {同志社大学理工学研究報告, The Science and Engineering Review of Doshisha University},
 month = {Oct},
 note = {本論文では,個々のユーザの持つ感性モデルを対話型遺伝的アルゴリズムを用いて同定し,その情報を用いた推薦手法を提案する.提案手法では,コンテンツの持つ特徴的な単語をコンテンツパラメータとして抽出し,単語間の類似度に基づく距離を有したコンテンツパラメータネットワークを構築した.このコンテンツパラメータネットワークによって構築された設計変数空間上で,対話型遺伝的アルゴリズムによる探索を進めることで,ユーザの感性モデルを構築し,よりユーザの感性に適したコンテンツの推薦を行う.実験では,被験者実験により本手法により類似したキーワードを主題とする商品が推薦結果に現れることを示した.この結果より,提案手法が適切に探索を進め,被験者の感性モデルに沿った情報が得られることを確認した., In this paper, we proposed a recommendation method using user's personal Kansei model, which was estimated by interactive Genetic Algorithm. When processing contents, this method extracts words which are representing the contents, and assigns these extracted words as content parameters. Then, this method constructs a contents parameter network in which the distance between nodes is defined by the similarities between them. By searching on a design variables space based on the contents parameter network, iGA estimates a user's Kansei model and recommends contents which are considered to be suitable for the user. In the experiment, the products recommended to a subject using the proposed method had the keywords which were similar to the characteristic of the products that he or she had already selected. This result indicated that the proposed method executed the searches properly, and obtained the contents which fitted his or her Kansei model., application/pdf},
 pages = {202--210},
 title = {個人の感性モデルを推定する商品推薦システム},
 volume = {54},
 year = {2013},
 yomi = {ミヤジ, マサヒロ and タナカ, ミサト and ヤマモト, ウタコ and ヒロヤス, トモユキ and ミキ, ミツノリ and ヨコウチ, ヒサタケ}
}