Electrical and Computer Engineering Publications
Integrating Recommender Information in Social Ecosystems Decisions
Document Type
Article
Publication Date
8-2010
URL with Digital Object Identifier
10.1145/1842752.1842783
Abstract
The exploration of online social ecosystems whose members share
mutual recommendations and interactions is a time-dependent and
contextual-based process which aims to predict the social status
among them. To address the difficulties associated with the
process, this article presents the integration of the predictive
recommender, social networks, and interaction components into a
single methodology. The originality of the proposed framework
stems from developing each model based on: (1) a time history
and decay algorithm to consider temporal recommendations and
interactions; (2) a predictive-aggregating function for different
types of social contexts; and, (3) a homophily algorithm to
evaluate people’s interconnections proximity. Details of the
framework are described, a recommender search strategy
methodology integrating all of the above is devised, and a case
study is used to demonstrate its capabilities. Possible extensions
are then outlined.
Citation of this paper:
@inproceedings{DBLP:conf/ecsa/CapurucoC10, author = {Renato A. C. Capuru\c{c}o and Luiz Fernando Capretz}, title = {Integrating recommender information in social ecosystems decisions}, booktitle = {ECSA Companion Volume}, year = {2010}, pages = {143-150}, ee = {http://doi.acm.org/10.1145/1842752.1842783}, crossref = {DBLP:conf/ecsa/2010c}, bibsource = {DBLP, http://dblp.uni-trier.de} } @proceedings{DBLP:conf/ecsa/2010c, editor = {Ian Gorton and Carlos E. Cuesta and Muhammad Ali Babar}, title = {Software Architecture, 4th European Conference, ECSA 2010, Copenhagen, Denmark, August 23-26, 2010. Companion Volume}, booktitle = {ECSA Companion Volume}, publisher = {ACM}, series = {ACM International Conference Proceeding Series}, year = {2010}, isbn = {978-1-4503-0179-4}, bibsource = {DBLP, http://dblp.uni-trier.de} }
