Recommendation Engine using Inferred Deep Similarities for Works of Literature

摘要

A recommendation engine for works of literature uses patterns of flow and element similarities for scoring a first user-rated work of literature against one or more recommendation candidate works of literature. Cluster models are created using meta-data modeling the works of literature, the meta-data having literary element categories and instances within each category. Each instance is described by an index value (position in the literature) and significance value (e.g. weight or significance). Cluster finding process(es) invoked for each instance in each category find Similarity Concept clusters and Consistency Trend clusters, which are recorded into the cluster models representing each work of literature. The cluster model can be printed or displayed so that a user can visually understand the ebb and flow of each literary element in the literature, and may be digitally compared to other cluster models of other works of literature for potential recommendation to a user.

主权项

1. A method for comparing and optionally recommending works of literature, comprising:
determining by a computer a degree of similarity between a first cluster model for a first work of literature and a second cluster model for a second work of literature; and producing by a computer a recommendation to a user regarding the degree of similarity; wherein the first and second cluster models reflect clusters of concepts and longitudinal patterns or consistency trends of elements in the clusters across multiple segments of each respective work of literature.