Narrow Results

We discuss the power and limitation of various "advice," when it is given particularly to weak computational models of one-tape linear-time Turing machines and one-way finite (state) automata. Of various advice types, we consider deterministically-chosen advice (not necessarily algorithmically determined) and randomly-chosen advice (according to certain probability distributions). In particular, we show that certain weak machines can be significantly enhanced in computational power when randomized advice is provided in place of deterministic advice.

Nowadays, there are a large number of users who post complaints about a certain service on the Internet. Because users have various values and views, even if they receive the same service, they may complain in different ways. However, it is quite difficult to respond to various user demands for service in real time and there are almost no direct solutions when users feel dissatisfied with a certain service. Therefore, in this paper, we propose a web search method by analyzing complaint data from Fuman Kaitori Center. First, the system generates query keywords according to various user complaints about a certain service by calculating the score of each query. Then suitable web pages containing advice are recommended from the results of the query. This advice could address users' dissatisfaction and respond to their various demands in a comprehensive way. Also, we verify the usability of proposed system by using a questionnaire survey evaluation.