Recommendation Engine using Inferred Deep Similarities for Works of Literature
First Claim
1. A method for comparing and optionally recommending works of literature, the method comprising:
- accessing by a processor a first cluster model for a first work of literature;
accessing by a processor at least a second cluster model for at least a second work of literature;
comparing by a processor the first and at least second cluster models for similarities;
generating by a processor a score according to degrees of match between the first and second cluster models; and
producing by a processor a recommendation score regarding the second work of literature as being comparable or similar to the first work of literature;
wherein the first and second cluster models reflect patterns of flow and element characterizing each respective work of literature.
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Abstract
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.
24 Citations
15 Claims
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1. A method for comparing and optionally recommending works of literature, the method comprising:
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accessing by a processor a first cluster model for a first work of literature; accessing by a processor at least a second cluster model for at least a second work of literature; comparing by a processor the first and at least second cluster models for similarities; generating by a processor a score according to degrees of match between the first and second cluster models; and producing by a processor a recommendation score regarding the second work of literature as being comparable or similar to the first work of literature; wherein the first and second cluster models reflect patterns of flow and element characterizing each respective work of literature. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification