Designing a model for context awareness based reliable auction-recommending system (CARARS) by utilizing advanced information

초록

This thesis aims to solve a problem that, even though the existing auction recommending system provides auction property based on the conditions and contexts users prefer, users cannot rely on the recommended property, but must analyze its investment value or request experts to analyze it. To solve this problem, advanced information in the real estate auction, an analysis of rights, an analysis of commercial quarters, and a development plan, are classified into 5 levels indicating investment value, which will be applied at the recommendation phase. This reliable recommending service is designed to be incorporated in the current context awareness system under a smart mobile environment. Therefore, we call it context awareness-based reliable auction recommending system (CARARS). © 2013 Springer Science+Business Media.

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