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Abstract

A new recommendation technique, referred to as 'personality diagnosis', that can be seen as a hybrid between memory-based and model-based collaborative filtering techniques is described. Using personality diagnosis, all data may be maintained throughout the process, new data can be added incrementally, and predictions have meaningful probabilistic semantics. Given an active entity's known attribute values, the probability that they have a same personality type as every other entity may be determined. Then, the probability that they will have a given value for a valueless attribute may be determined based on the entity's personality type. The probabilistic determination may be used to determine expected value of information that could be used to favorably order queries for attribute values, thereby mollifying what could otherwise be a tedious and frustrating process and/or to determine which entries of a database if removed would have a minimal effect of the accuracy of recommendations.

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First Claim