Abstract
A basic component of automated matchmaking is the automatic generation of a ranked list of profiles matching with the profiles of a given participant. Identifying and ranking of matching profiles among thousands of candidate profiles is a challenging task. In order to determine the degree of matching between two profiles, corresponding pairs of constraints are compared and aggregated to the overall similarity between the two profiles.
This paper describes the structure and algorithm of a proposed matchmaking system with a focus on the central notion of compromise match. A compromise match is called for when either one or both constraints within a pair are soft and moreover their values do not match exactly. Two important aspects of compromise matching are discussed, namely compromise count factor, compromise count reduction factor; furthermore their effect on ranking is described. A use case with a sample set of home rental profiles from an existing e-marketplace is employed for demonstration.
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Joshi, M., Bhavsar, V.C., Boley, H. (2011). Compromise Matching in P2P e-Marketplaces: Concept, Algorithm and Use Case. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_34
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DOI: https://doi.org/10.1007/978-3-642-25725-4_34
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