By Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.), Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger (eds.)
This quantity constitutes the refereed complaints of the sixth overseas Workshop on Adaptive Multimedia Retrieval, AMR 2008, held in Berlin, Germany, in June 2008.
Read Online or Download Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music: 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers PDF
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Additional resources 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). This means for the poset that the following holds: ∀ o ∈ R : (o ≥ o) ∈ P Preference arrangement: Users can arrange the order of two objects from R to express their different reception of similarity or relevance for the two elements (see Fig. 1: o4 and o5 ). Trash bin: Objects can be moved to a trash bin in case they are not relevant to the query. This gives the elements with the lowest relevance: ∀ o ∈ R : (o† ≤ o) ∈ P Pinboard: An additional pinboard should be provided to save objects that are not directly relevant to the query but might become useful to the user in later tasks.
Metadata is rarely given for this content and relations to other artists are hardly to detect. To overcome these problems, content-based music retrieval (CBMR) models the acoustic characteristics of music and computes similarities between songs in the model space. Recommendation is realized by analyzing the content of audio ﬁles. However, human perception of music similarity can diﬀer from person to person and situation to situation. Presumably, a song is represented by characteristics the user likes and dislikes.
Below we summarize the most important, preliminary, findings. During the evaluation, 32 participants were given the task of searching for youth hostels and museums of modern art in large European cities. These tasks have been chosen because the related information is relatively easy to find and is presented on many websites categorized by ODP. Thus, a user profile that ensures a high degree of personalization on these topics can be created in a small amount of time. For one city, the information had to be found with Google, for four cities with Prospector.
Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music: 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers by Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.), Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger (eds.)