A large number of people download music files easily from web sites. But rare music sites provide personalized services. So, we suggest a method for personalized services. We extract the properties of music from music's sound wave. We use STFT (Shortest Time Fourier Form) to analyze music's property. And we infer users' preferences from users' music list. To analyze users' preferences we propose a dynamic K-means clustering algorithm. The dynamic K-means clustering algorithm clusters the pieces in the music list dynamically adapting the number of clusters. We recommend pieces of music based on the clusters. The previous recommendation systems analyze a user's preference by simply averaging the properties of music in the user's list. So those cannot recommend correctly if a user prefers several genres of music. By using our K-means clustering algorithm, we can recommend pieces of music which are close to user's preference even though he likes several genres. We perform experiments with one hundred pieces of music. In this paper we present and evaluate algorithms to recommend music.

In this paper, we propose a music recommendation system based on user preference analysis. The system builds music models using Hidden Markov Models with Mel Frequency Cepstral Coefficients, which are features of sound wave. Each song is modeled with an HMM and the similarity measure between songs are defined based on the models. With the similarity measure, the songs the user listened to in the past are grouped and analyzed. The system recommends pieces of music to the user based on the result of the analysis. We evaluate our system with virtual users who have various preferences, and observe which recommendation lists the system generates. In most cases, the system recommends the pieces of music which are close to user's preference.

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Blog is a personal publishing tool which encourages users to contributions in the Web. As the number of blog entries and contributors (bloggers) grows at a very fast pace, they are increasingly fiDing the Web space. Thus effective search in the blogspace become more important. For effective search, the page ranking algorithm is one of the most critical techniques. Blogs have the structural features, which do not exist in the traditional Web, such as trackback links, tags, comments. For this reason, the page ranking algorithms for the traditional Web may not work effectively in the blogspace. In this paper, we propose a new "Trackback-Rank" algorithm which considers the features of blogs for more effective blog search. We evaluate bloggers' authority, trackback connectivity, and users' reactivity in order to rank blog entries. These factors are created and modified by the interaction among blog users. The blog users read and evaluate contents of blog entries and then interaction other users. Thereby, these factors implicitly reflect the contents quality of the entries, and the Trackback-Rank algorithm could improve the relevance of the search result to the queries. Our experiments on a collection of 62,906 blog entries shows that the Trackback-Rank algorithm can more effectively find relevant information compared to the traditional ranking algorithm.

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