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In general, content-based recommender systems use a keyword vector to locate recommendations. However, this method does not consider relations of each keyword and it is also inscrutable to users, who may have a hard time determining which words in their profiles are important and which may be skewing their results to
their personal file system by leveraging semantic relationships available on the Web. More specifically, JabberWocky is using keyword/resource associations of social bookmarking web sites as a basis for recommending keywords for files. We chose social bookmarking web sites because of their popularity and because the
This paper proposes the method to recommend music using lyric network. This method corresponding to more than thousands of musics. The authors focus each lyric of the music. Keywords representing music are extracted from its lyric by combining TF-IDF method and principle of discriminant analysis. Lyric network is
by the collaborative use of audio and textual features obtained from music videos and social metadata “related videos”, “tags”, and “keywords” through sub-sampled canonical correlation analysis. By performing link prediction on the obtained network, our method enables users to
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.