Recommendation engine using inferred deep similarities for works of literature

• US 9,613,098 B2
• Filed: 09/19/2014
• Issued: 04/04/2017
• Est. Priority Date: 12/03/2013
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Allen, Corville O., Carrier, Scott R., Woods, Eric
• Primary Examiner(s): Hwa, Shyue Jiunn
• Application Number:
  US14/491,052
• Publication Number:
  US 20150154278A1
• Time in Patent Office: 928 Days
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/38 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/93 Document management systems

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.

Claims

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 clusters of concepts and longitudinal trends of elements in the clusters across multiple segments contained entirely within each respective work of literature.

2. The method as set forth in claim 1 wherein the first cluster model comprises at least one cluster model for which a first user has indicated at least one literary element category to be of interest or disinterest.

3. The method as set forth in claim 1 wherein the at least second cluster model comprises at least one cluster model for which at least a second user has indicated at least one literary element category to be of interest or disinterest.

4. The method as set forth in claim 1 wherein the at least second cluster model comprises at least one cluster model for which a first user has indicated at least one literary element category to be of interest or disinterest.

5. The method as set forth in claim 1 wherein the accessing of a first cluster model further comprises:

receiving by a processor meta-data modeling the first work of literature, the meta-data comprising one or more literary element categories, one or more instances within each literary element categories, and an index value and significance value for each instance, wherein the index value corresponds to a position for an instance between a beginning and an ending of the work of literature, and wherein the significance value corresponds to a significance, weight or strength of an instance relative to other instances in the meta-data;

for each literary element category and for each instance within each literary element category of the first work of literature, invoking by a processor a cluster finding process using a first control parameter limiting the range of index value variation and a second control parameter limiting the range of significance value variation in a found cluster;

receiving by a processor from the cluster finding process one or more clusters found around one or more instances of one or more literary element categories; and

storing by the processor the one or more clusters into the first cluster model for the first work of literature.

6. The method as set forth in claim 5 wherein the first control parameter limiting the range of index value variation is set to a value less than an entire length of the first work of literature.

7. The method as set forth in claim 5 wherein the first control parameter limiting the range of index value variation is set to a value less than a chapter or less than a section of the first work of literature.

8. The method as set forth in claim 5 wherein the cluster finding process is controlled by the first and second control parameters to find a Similarity Concept cluster existing locally or regionally within the work of literature.

9. The method as set forth in claim 5 wherein the cluster finding process is controlled by the first and second control parameters to find a Consistency Trend cluster extending longitudinally within the index range of the work of literature.

10. The method as set forth in claim 5 wherein the receiving of meta-data comprises receiving machine-generated meta-data modeling the work of literature.

11. The method as set forth in claim 5 wherein the receiving of meta-data comprises converting manually-created review information into machine-generated meta-data modeling the work of literature.

12. The method as set forth in claim 5 further comprising plotting by the processor the cluster model to a human interface output device.

13. The method as set forth in claim 12 wherein the human interface output device is a computer display.

14. The method as set forth in claim 12 wherein the human interface output device is a printer.

15. The method as set forth in claim 5 further comprising outputting by the processor the cluster model to a digital image file.