深澤 佑介

we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.we propose a method of predicting human's activity, including the location and purpose, by using Twitter posts with location information. The proposed method predicts target users' activities based on the location transition and tweet of users in the database. Concretely, we adopt both the similarity of current location and interest, and the similarity of long term interest and location to select the base user and tweet. And then, we can utilize these two baselines to predict target users' activities. We evaluate the proposed method by the following two points: one is the error range of the distance, and the other is the similarity of tweet contents. We used three months of Twitter data with location information (almost 40 mil.) as the database. The experiment results demonstrate that the prediction accuracy of the proposed method is superior to the two control groups which only consider one of the similarity of current location and interest and the similarity of long term interest and location.

The web is thriving with websites containing How-To articles and DIY guides. In websites like that information about activities and guides how to accomplish them are located. An activity is defined as any action that a person can accomplish. Our research focuses on Retrieving activities from the Web related to a user's search query. To accomplish that we have created a system that performs meta-search in multiple How-To websites using semantic query expansion. An overview of the activities retrieval system is presented along with the explanation of the query expansion algorithm which utilizes ConceptNet. Finally experimental results gathered from a user study are given in order to evaluate the performance of our system.The web is thriving with websites containing How-To articles and DIY guides. In websites like that information about activities and guides how to accomplish them are located. An activity is defined as any action that a person can accomplish. Our research focuses on Retrieving activities from the Web related to a user's search query. To accomplish that we have created a system that performs meta-search in multiple How-To websites using semantic query expansion. An overview of the activities retrieval system is presented along with the explanation of the query expansion algorithm which utilizes ConceptNet. Finally experimental results gathered from a user study are given in order to evaluate the performance of our system.

本稿では，ユーザの実世界行動モデルの構築を軽減するため，クラスタリング手法の適用可能性を検証する．検証実験の結果，マニュアルで構築したモデルと 50％ 程度重複するモデルを構築することができた．現状，実世界行動モデルをマニュアルで構築するには 1 ヶ月程度かかることから，クラスタリングとマニュアルを組み合わせることで構築に要する時間を劇的に削減できると考えられる．To construct user's real world activity model in an automatic way, in this paper, the possibility to use the clustering method is verified. As the experimental result, the taxonomic overlap between model constructed with clustering and manually constructed reference model becomes almost 50%. Considering that we need one month to construct the real world activity model buy manual, this result indicates that the time required constructing the model will be reduced dramatically by using the clustering with manual way of constructing the model.