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With the development of the Internet, the users share information using Web applications. Because of this reason, there is lots of information on the Web. The information includes not only high quality information, but also useless one. With the phenomena, the recommendation system appears on the Web. Existing information recommendation systems on the Web have known problems. One famous problem is cold-start. We tackle the cold-start problem for a new item in recommendation system. To alleviate cold-start for a new item, we use method for identifying representative reviewers in raters group and recommendation algorithm based on category correlations. The representative reviewers mean the users who represent their raters group. Namely, the ratings of the reviewers can represent the average ratings of other users. If there are the ratings for new items rated by the representative reviewers, then we can consider the ratings rated by many other users. We predict the ratings of these reviewers for a new item. To predict ratings, we use the recommendation algorithm based on the category correlations. This algorithm can draw the prediction results without ratings since the algorithm uses category information. We propose the prediction results of the representative reviewers as the representative ratings for a new item. We propose the algorithm to alleviate cold-start for a new item and show the reliability of our approach through tests.

Since the late 20th century, the number of Internet users has noticeably increased. Recently, the number of Internet queries and the quantity of information available on the web has increased drastically. A large amount of new information is uploaded to the Web on a daily basis. However, search results are not always reliable due to the vast amount of data available on-line. As a result, users often have to repeat their searches in order to find exactly what they are looking for. To remedy this, some researchers have suggested recommendation systems. Since a recommendation system proposes information relevant to a particular query, users no longer need to repeat a search to obtain desired data. In the Web 2.0 era, recommendation systems often rely on the collaborative filtering approach, which is based on user information such as age, location, or preference. However, the traditional approach is affected by the cold-start and sparsity problems. The reason for these problems is the fact that the traditional system requires user information to operate properly. In this paper we address the sparsity problem associated with the current recommendation systems. We also suggest a new recommendation system approach and compare the performance of the proposed method with that of the traditional approach.

Since the late 20th century, the Internet users have noticeably increased and these users have provided lots of information on the Web and searched for information from the Web. Now there are huge amount of new information on the Web everyday. However, not all data are reliable and valuable. This implies that it becomes more and more difficult to find a satisfactory result from the Web. We often iterate searching several times to find what we are looking for. Researcher suggests a recommendation system to solve this problem. Instead of searching several times, a recommendation system proposes relevant information. In the Web 2.0 era, a recommendation system often relies on the collaborative filtering from users. In general, the collaborative filtering approach works based on user information such as gender, location or preference. However, it may cause the cold-star problem or the sparsity problem since it requires initial user information. Recently, there are several attempts to tackle these collaborative filtering problems. One of such attempts is to use category correlation of contents. For instance, a movie has genre information given by movie experts and directors. We notice that these category information are more reliable compared with user ratings. Moreover, a newly created content always has category information; namely, we can avoid the cold-start problem. We consider a movie recommendation system. We revisit the previous algorithm using genre correlation and improve the algorithm. We also test the modified algorithm and analyze the results with respect to a characteristic of genre correlations.

Most industrial designers work with a pencil and some plane papers. In spite of the paper-based plane modeling data by these designers, the majority of the industrial modeling data for manufacturing are based on the spatial concept. While converting the designer's 2D drawing data to the digital form of 3D industrial modeling, designer's intention is often misunderstood or misinterpreted. If the designer can use the spatial modeling tool in their conventional way, it is easy to express their intention and there is no need to transform the 2D drawings to 3D model.