Recommendation engine using inferred deep similarities for works of literature
US10108673B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 6, 2016 |
| Grant date | Oct 23, 2018 |
| Priority date | — |
| Expiry date | Jul 13, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/93
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.