Electrical and Computer Engineering Publications

Title

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.

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