Using Bayesian Networks To Infer Product Rankings From User Needs

Provided by: RWTH Aachen University
Topic: Data Management
Format: PDF
When recommending complex products to customers, a knowledge-based recommender system has to base its reasoning on the mostly technical product properties that can be obtained using datasheets or similar sources of information. Customers are commonly not able to provide preferences that are technical enough to be used in the internal algorithms of knowledge-based recommender systems. In this paper, the authors present an approach to use a Bayesian network to infer technical preferences from customer answers obtained through a conversational elicitation process.

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