Abstract: Genre discovery engines are presented. A genre discovery engine can compare clusters of products falling within known genres to other clusters. Known genres can be defined in turns of correlated product properties. When a new cluster is identified falling outside the boundaries of known genres, the discovery engine can recommend that the new cluster might be a new genre.

Abstract: A product recommendation ecosystem is presented. A rules engine seeks to discover one or more relationships among cross-brand product categories based on non-transaction correlations. The rules engine constructs a generic rules-set based on the universal relationships. The rules-set is sent to a recommendation engine, possibly a subscriber to the services offered by the rules engine, and the rules-set configure the recommendation engine to generate one or more cross-brand product recommendations. The recommendation engine customizes the rules-set according to location-specific information possibly comprising consumer parameters, product parameters, vendor parameters, or other local information.

Abstract: Review bias identification systems and methods are presented. A bias in one or more review elements can be identified by deriving a measure of how a review outlet's product review deviates from an industry average or composite review of the product. A bias engine generates a bias vector for a review outlet where the vector can include multiple bias metrics associated with one or more product properties. The bias engine can further present one or more recommendations of associating the product with a review outlet based on the bias vector.