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 foreign 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
6. Fig. 3. Estimated distribution of objects in the poset Another approach exploits the list semantics of the original result set R. Given a list ordered by relevance, the user only rearranges the objects that do not conform his perception of relevance in addition to his choice of favorite and irrelevant results. The main difference to the first approach is that objects, which have not been touched by the user, keep their position in the list and are therefore included in the derived poset according to their original order.
40–52, 2010. c Springer-Verlag Berlin Heidelberg 2010 Adaptive User Modeling for Content-Based Music Retrieval 41 the user, his relevance feedback needs to be captured and incorporated into the recommendation process. 2 Related Work In , relevance feedback is incorporated by training a support vector machine (SVM) with songs, provided by the user as training samples. The system then iterates between proposing new songs to the user and training a new classiﬁer on the labeled songs. This process is denoted active learning.
DB conditions. A Poset Based Approach for Condition Weighting 35 In our opinion, only the first three approaches can be recommended. Especially, the first initial weighting scheme is useful in general research tasks and can be used directly without prior knowledge. It generates reasonably good results by exploiting additional informations from the user interaction that can only be exceeded by the domain or user specific profile approaches. 4 Algorithm In our example query, see figure 1, we combine four conditions: one similarity condition, two database conditions, and one spatial proximity condition.
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.)