Hybrid Explanations In Collaborative Filter Based Recommendation System

摘要

Example apparatus and methods produce an explanation of why a recommendation is being made by an automated collaborative filtering recommendation system. The explanation may include feature categories and features that describe the item being recommended. The feature categories selected and features selected may depend on a personalization level for an item associated with the recommendation, a quality level of the descriptiveness of a feature for the recommendation, and correlations between items and features analyzed by the recommendation system. The feature categories and features may be selected based on an aggregate score that considers and combines the personalization level, the quality level, and the correlations. The quality level may be human curated or may vary directly with the ability of a feature to partition a feature space. Correlations between items and features reflect the degree to which the features are exhibited by the items.