By Matthias Geier, Sascha Spors, Stefan Weinzierl (auth.), Marcin Detyniecki, Ulrich Leiner, Andreas Nürnberger (eds.)
This quantity constitutes the refereed lawsuits of the sixth overseas Workshop on Adaptive Multimedia Retrieval, AMR 2008, held in Berlin, Germany, in June 2008.
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Extra 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
With regard to , nearest neighbor distances of candidate songs to the labeled songs are computed to provide the user new recommendations. 1 Concept System Overview An overview of the system components is given in Fig. 1. The recommendation engine (RE) creates a playlist sorted by the distance to a given song. This song is speciﬁed by the user and will be denoted as seed song. The user feedback system (UFB system) analyzes the user’s relevance feedback about already played songs and adapts the playlist accordingly.
Color = 'black and white' 3. category = 'socialist realism' 4. location ~ 'Berlin/Brandenburg' Fig. 1. Example multimedia retrieval query sample image5 , which are taken in black and white, belong to the art style category ‘socialist realism’ and are located in the geographical area of ‘Berlin/Brandenburg’ (see Fig. 1). This query combines an implicit similarity query which is based on low-level features (1) with two database attribute comparisons (2,3) and a spatial proximity condition (4). Unfortunately, this common query-by-example (QBE) approach combined with keywords neither reveals the subjective relative importance of the conditions for the user, nor their logical connection (conjunction or disjunction).
4 Ontology-Based Reordering of Search Results The re-ranking of results is based on the likelihood that a given result will be of interest to a given user. 1 above. P(u, r) denotes the likelihood that user u is interested in item r. This is defined as follows: 24 A. Paramythis et al. 5 − If the user’s individual model already includes a value for c(r) (on the basis of previous user ratings), that value is used verbatim − If the user’s individual model does not include a value for c(r), then derive a value from the models of groups in which the user is interested, as well as from the user’s own model, if the later contains values for ancestor categories of c In the context of the third of the above cases, the following definitions are made: Pgroup(u, c) denotes the likelihood that user u is interested in category c, as derived from the models of groups in which the user is interested; Pinherited(u, c) denotes the likelihood that user u is interested in category c, as calculated from the user’s own model, using ancestor categories of c.