Jonathan Huang

Techniques are disclosed for generating and ranking product recommendations based at least in part on product attributes. Two or more sets of product recommendations may be generated based on a source product. The recommendation sets may include products also purchased by those who purchased the source product, products within the same genre as the source product, or products with some other trait in common with the source product. The product recommendations may be initially ranked based on… Show more

Techniques are disclosed for generating and ranking product recommendations based at least in part on product attributes. Two or more sets of product recommendations may be generated based on a source product. The recommendation sets may include products also purchased by those who purchased the source product, products within the same genre as the source product, or products with some other trait in common with the source product. The product recommendations may be initially ranked based on overlap within the recommendation sets. A product attribute relating to the source product or one or more of the product recommendations may be determined, and this attribute may be correlated with the product recommendations. The recommendations may then be re-ranked based on the correlated product attribute and a product recommendation list may be displayed to the user. The recommendation list may be limited to a particular type of product using a control filter. Show less

A methods to generate user profile based on user's affinity to products for personalized discovery experiences.

A system that generates user profile based on user's affinity to products for personalized discovery experiences.

The present invention creates a new taxonomy called Reader Categories that incorporates a bookseller's bookselling knowledge to generate more accurate and compelling recommendations to users. An initial "seeding" of the Reader Categories with content is performed by an editorial staff. A recommendation engine is then executed with respect to the initial seeds to generate recommendation of additional content for the Categories. A tool is provided that the editorial staff can use for the seeding… Show more

The present invention creates a new taxonomy called Reader Categories that incorporates a bookseller's bookselling knowledge to generate more accurate and compelling recommendations to users. An initial "seeding" of the Reader Categories with content is performed by an editorial staff. A recommendation engine is then executed with respect to the initial seeds to generate recommendation of additional content for the Categories. A tool is provided that the editorial staff can use for the seeding and for providing feedback on the quality of algorithmically generated results. This helps the present invention extend the power of its recommendation algorithms by facilitating editorial ranking and seeding. Show less

Techniques are disclosed for providing product recommendations based on content clusters. The product may be, for example, goods or services. In some embodiments, the techniques include forming a product cluster based at least in part on product metadata, correlating the product cluster based at least in part on product correlation data, and calculating each product distance to a center of each correlated product cluster. In some cases, the techniques may further include generating… Show more

Techniques are disclosed for providing product recommendations based on content clusters. The product may be, for example, goods or services. In some embodiments, the techniques include forming a product cluster based at least in part on product metadata, correlating the product cluster based at least in part on product correlation data, and calculating each product distance to a center of each correlated product cluster. In some cases, the techniques may further include generating recommendations based on product clusters, wherein only products within a given distance to a center of each correlated product cluster are recommended. In some cases, forming a product cluster is carried out using k-means clustering so as to minimize the within-cluster sum of squares, and the techniques may further include optimizing the within cluster sum of squares. Show less