The aim of this study is to calculate optimal walking routes in real space for users partaking in immersive virtual reality (VR) games without compromising their immersion. To this end, we propose a navigation system to automatically determine the route to be taken by a VR user to avoid collisions with surrounding obstacles. The proposed method is evaluated by simulating a real environment. It is verified to be capable of calculating and displaying walking routes to safely guide users to their destinations without compromising their VR immersion. In addition, while walking in real space while experiencing VR content, users can choose between 6-DoF (six degrees of freedom) and 3-DoF (three degrees of freedom). However, we expect users to prefer 3-DoF conditions, as they tend to walk longer while using VR content. In dynamic situations, when two pedestrians are added to a designated computer-generated real environment, it is necessary to calculate the walking route using moving body prediction and display the moving body in virtual space to preserve immersion.

In recent years, user-supplied reviews have increased to become widely prevalent on many websites. Some reviewers (users who comment on items) provide valuable information. Others provide information many people already know. Our goal is to identify credible reviewers who provide valuable information. Two methods can be used to measure reviewer credibility: assessing reviewers based on the content of reviews that they have written in the past and assessing reviewers based on their review histories. By comparing these methods, we aim at obtaining knowledge to determine which method is most useful for identifying credible reviewers. Additionally, many features have been proposed for assessing reviews or reviewers in the previous methods, but they have not been compared. We compare these attributes and clarify what kinds of attribute are useful for identifying credible reviewers.

Recently, semantic text portion (STP) is getting popular in the field of Web mining. STP is a text portion in the original page which is semantically related to the anchor pointing to the target page. STPs may include the facts and the people's opinions about the target pages. STPs can be used for various upper-level applications such as automatic summarization and document categorization. In this paper, we concentrate on extracting STPs. We conduct a survey of STP to see the positions of STPs in original pages and find out HTML tags which can divide STPs from the other text portions in original pages. We then develop a method for extracting STPs based on the result of the survey. The experimental results show that our method achieves high performance.

Net auctions have been widely utilized with the recent development of the Internet. However, it is a problem that there are too many items for bidders to select the most suitable one. We aim at supporting the bidders on net auctions by automatically generating a table which contains the features of several items for comparison. We construct a system called NTM-Agent (Net auction Text Mining Agent). The system collects web pages of items and extracts the items' features from the pages. After that, it generates a table which contains the extracted features. This research focuses on two problems in the process. The first problem is that if the system collects items automatically, the results contain the items which is different from the items of the user's target. The second problem is that the descriptions in net auctions are not uniform (There are different formats such as sentences, items and tables. The subjects of some sentences are omitted. ). Therefore, it is difficult to extract the information from the descriptions by conventional methods of information extraction. This research proposes methods to solve the problems. For the first problem, NTM-Agent filters the items by correlation rules about the keywords in the titles and the item descriptions. These rules are created semi-automatically by a support tool. For the second problem, NTM-Agent extracts the information by distinguishing the formats. It also learns the feature values from plain examples for the future extraction.