We propose an algorithm for recommending both well-watched old movies and unwatched new ones. To recommend both old favourites and new releases, hybrids of collaborative and content-based filtering are the most suitable methods. However, hybrid movie recommenders have two issues. First, it is necessary to acquire content-descriptive metadata, which is not always easily available. Second, the metadata, once acquired, may be noisy, which can damage recommendation accuracy. In our algorithm, we address the first issue by automatically drawing movie metadata from Linked Open Data, and the second by modeling the relevance of the collected metadata to the transaction history before using the relationship between them to make recommendations. We experimentally demonstrate that our method can effectively collect metadata from LOD, and that our method outperforms conventional hybrid methods found in the literature in both well-watched and unwatched movie recommendation using the noisy collected movie metadata.

Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.

In order to create more attractive tagclouds that get people interested in tagged content, we propose a simple but novel tagcloud where font size is determined by tag's entropy value, not the popularity to its content. Our method raises users' emotional interest in the content by emphasizing more emotional tags. Our initial experiments show that emotional tagclouds attract more attention than normal tagclouds at first look; thus they will enhance the role of tagcloud as a social signaller.

A novel method for multispectral image retrieval is presented. This method uses a representation of image features based on vector quantization. Feature representation is important for image retrieval, but there are difficulties in applying conventional histogram-based representations to multispectral images. We developed an efficient feature representation and a novel method for the retrieval of images by quantizing each image adaptively.

In this paper a novel method for the unsupervised segmentation of textured images is presented. Textures are modeled as spatial interactions between pixels. Thus, a certain window size is required to extract texture features and to estimate texture boundaries by using the features. As long as the size of the window is fixed over the whole of an image, we cannot accurately estimates texture regions that have similar properties. In this paper the problem of selection of an appropriate size for window used to estimate homogeneous texture regions is investigated via hypothesis and testing. Experiments on segmentation of textures in synthetic and natural images show the effectiveness of the method.

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results.>