A data-mining approach to preference-based data ranking founded on contextual information
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摘要
The term information overload was already used back in the 1970s by Alvin Toffler in his book Future Shock, and refers to the difficulty to understand and make decisions when too much information is available. In the era of Big Data, this problem becomes much more dramatic, since users may be literally overwhelmed by the cataract of data accessible in the most varied forms. With context-aware data tailoring, given a target application, in each specific context the system allows the user to access only the view which is relevant for that application in that context. Moreover, the relative importance of information to the same user in a different context or, reciprocally, to a different user in the same context, may vary enormously; for this reason, contextual preferences can be used to further refine the views associated with contexts, by imposing a ranking on the data of each context-aware view. In this paper, we propose a methodology and a system, PREMINE (PREference MINEr), where data mining is adopted to infer contextual preferences from the past interaction of the user with contextual views over a relational database, gathering knowledge in terms of association rules between each context and the relevant data.
论文关键词:Data personalization,Context-awareness,Pervasive computing,Context-aware preference systems
论文评审过程:Received 27 September 2012, Revised 3 December 2012, Accepted 4 December 2012, Available online 20 December 2012.
论文官网地址:https://doi.org/10.1016/j.is.2012.12.002