By Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.), Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger (eds.)
This quantity constitutes the refereed court cases of the sixth overseas Workshop on Adaptive Multimedia Retrieval, AMR 2008, held in Berlin, Germany, in June 2008.
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Additional info for Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music: 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers
1 Introduction In the recent years, the way to acquire and share music has changed. New distribution channels like the internet extend conventional channels, due to the availability of broader bandwidth and techniques for audio compression. Music is now provided as downloads, which leads to new business models such as music ﬂatrate services. The increasing amount of available music can hardly be handled with traditional methods. Metadata merely gives a coarse description of the content, assumed that this information is correct or existing at all.
We use R to denote the set of search result items returned by a query q, and c(r) (or, simply, c) to denote the primary ODP category associated with a search result item r, with r ∈ R . This category is derived as follows. Let C(r) be the set of all ODP categories associated with result item r. ) associated with that category. , Depth(“Top/Computers/Software”) = 3). 2 User Modeling Using Search Result Item Ratings As already seen, users have the possibility to rate a specific search result r. Such ratings modify the likelihood that the user is interested in the respective category c(r) – denoted as c for brevity.
A straightforward approach to form mid-level features is computing the mean and standard deviation of time series of low-level features. Additionally, some techniques are described in . High-level features are designed to provide semantic information such as full music annotation, denoted as aspect, 42 K. Wolter, C. Bastuck, and D. G¨ artner Fig. 1. System overview or a transcription of the audio signal. A single aspect is created by a semantic classiﬁer, which uses supervised learning on a given set of training samples ().