Advances in Ubiquitous User Modelling: Revised Selected by Tsvi Kuflik, Shlomo Berkovsky, Francesca Carmagnola,

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.

Show description

Read or Download Advances in Ubiquitous User Modelling: Revised Selected Papers PDF

Best user experience & usability books

Everyday Innovators: Researching the Role of Users in Shaping ICTs (Computer Supported Cooperative Work)

Daily Innovators explores the lively function of individuals, jointly and separately, in shaping using details and conversation applied sciences. It examines matters round buying and utilizing that wisdom of clients, how we must always conceptualise the position of clients and comprehend the types and barriers in their participation.

Computer-Supported Collaboration: With Applications to Software Development

Computer-Supported Collaboration with purposes to software program improvement stories the speculation of collaborative teams and the criteria that have an effect on collaboration, really collaborative software program improvement. The impacts thought of derive from varied assets: social and cognitive psychology, media features, the problem-solving habit of teams, technique administration, crew details processing, and organizational results.

Creativity in the Digital Age

This edited booklet discusses the fascinating box of electronic Creativity. via exploring the present country of the inventive industries, the authors convey how applied sciences are reshaping our inventive procedures and the way they're affecting the cutting edge construction of recent items. Readers will notice how artistic construction approaches are ruled by means of electronic facts transmission which makes the relationship among humans, rules and inventive approaches effortless to accomplish inside collaborative and co-creative environments.

Computational Autism

This publication explores and evaluates bills and types of autistic reasoning and cognition from a computational point of view. the writer investigates the constraints and peculiarities of autistic reasoning and units out a remediation technique to be utilized by a variety of psychologists and rehabilitation team of workers and also will be favored by way of machine scientists who're attracted to the sensible implementation of reasoning.

Extra info for Advances in Ubiquitous User Modelling: Revised Selected Papers

Sample text

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 [39]. 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.

Download PDF sample

Rated 4.90 of 5 – based on 50 votes