Adapting the interactive activation model for context recognition and identification
In this article, we propose and implement a new model for context recognition and identification. Our work is motivated by the importance of "working in context" for knowledge workers to stay focused and productive. A computer application that can identify the current context in which the knowledge worker is working can (among other things) provide the worker with contextual support, for example, by suggesting relevant information sources, or give an overview of how he or she spent his or her time during the day. We present a descriptive model for the context of a knowledge worker. This model describes the contextual elements in the work environment of the knowledge worker and how these elements relate to each other. This model is operationalized in an algorithm, the contextual interactive activation model (CIA), which is based on the interactive activation model by Rumelhart and McClelland. It consists of a layered connected network through which activation flows. We have tested CIA in a context identification setting. In this case, the data that we use as input is low-level computer interaction logging data. We found that topical information and entities were the most relevant types of information for context identification. Overall the proposed CIA model is more effective than traditional supervised methods in identifying the active context from sparse input data, with less labelled training data. © 2016 ACM.
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DSC - Data Science
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Interactive activation model
Association for Computing Machinery
ACM Transactions on Interactive Intelligent Systems, 6 (3), 1-30