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Integrating Recommender Information in Social Ecosystems Decisions

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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} }

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