By Tsvi Kuflik, Shlomo Berkovsky, Francesca Carmagnola, Dominikus Heckmann, Antonio Krüger
Ubiquitous person modeling differs from frequent consumer modeling through 3 extra thoughts: ongoing modeling, ongoing sharing, and ongoing exploitation. platforms that percentage their consumer types will enhance the assurance, the extent of aspect, and the reliability of the built-in consumer types and hence enable larger features of version. Ubiquitous person modeling implies new demanding situations of interchangeability, scalability, scrutability, and privacy.
This quantity offers result of a chain of workshops related to Ubiquitous consumer Modeling considering that 2003 and extra workshops at quite a few different meetings e.g. on consumer Modeling and Adaptive Hypermedia within the final 4 years.
The eight revised complete papers offered have been rigorously reviewed and chosen from the simplest lectures given on the workshops and have been considerably prolonged to be integrated within the book.
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Extra info for Advances in Ubiquitous User Modelling: Revised Selected Papers
We consider each micro-context as a vector of keywords in the form of strings. To compare the elements in the vectors, we use the Dice coefficient - a term based similarity measure - used in Information Retrieval . g. Rock as music genre or Rock as geological object. The neighbors of C = object(s1R ) are a sub part of the Wordnet synset of C = object(s1R ). 30 F. Carmagnola Fig. 3. 0 indicates orthogonal vectors. The Dice coefficient measures the similarity of two vectors X and Y : DC(X, Y ) = 2 ∗ |X ∩ Y | |X| + |Y | The Osm algorithm returns the Dice coefficient of both micro-contexts: 1 1 Osm(object(sP ), object(sR )) = DC(microContext(object(sP ), ΩP ), microContext(object(sR ), ΩR ) which indicates the measure of the semantic similarity among object(sP ) in SP and object(s1R ) in s1R .
6 > If the receiver R needs to know Carlo’interest for Rock music (expressed trough the property has interest), it does not care about Carlo’s knowledge in Rock (expressed trough the property has knowledge) or his preferences about Rock music. Therefore, measuring the similarity among property(sP ) and property(s1R ) is necessary in establishing the similarity among (sP ) and (s1R ). To this purpose and differently from the Osm, we cannot rely on the domain ontology of the provider, which may not include a taxonomy of the properties.
A final remark regards the Relevance Algorithm. The performance of the Object Similarity Algorithm is strictly related to the level of granularity used in defining the taxonomy of the classes in the ontologies. Both in the case the classes of an ontology are represented with a thick level of granularity as well as they are represented with 34 F. Carmagnola a too low level of granularity, the comparison of vectors will be not significant and the Osm algorithm will result an object similarity measure that does not overcome the score of threshold.