Recommendation engine using inferred deep similarities for works of literature

• US 10,108,673 B2
• Filed: 05/06/2016
• Issued: 10/23/2018
• Est. Priority Date: 12/03/2013
• Status: Active Grant

• 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:
  US15/148,733
• Publication Number:
  US 20160253392A1
• Time in Patent Office: 900 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 digital literature, comprising:

performing, by a processor, deep semantic analysis to create at least a first digital cluster model for at least a first work of digital literature, wherein the deep semantic analysis comprises machine learning by the computing platform, and wherein the cluster model contains clusters which are limited within a range of significance values for one or more instances within one or more literary element categories;

determining, by a computer, a degree of similarity between the first digital cluster model for a first work of digital literature and a second digital cluster model for a second work of digital literature; and

producing, by a computer, via a user interface device, a recommendation to a user regarding the degree of similarity;

wherein the first and second cluster models one or more clusters selected from the group consisting of abstracted concepts, abstracted longitudinal patterns, and abstracted consistency trends of elements in the clusters across multiple segments of each respective work of digital literature, wherein the abstracted concepts, abstracted relationships, abstracted longitudinal patterns, and abstracted consistency trends of elements were abstracted from actual elements in the respective works of digital literature by deep semantic analysis.

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 2 wherein the second cluster model comprises a cluster model for which the first user has indicated at least one literary element category to be of interest or disinterest.

4. The method as set forth in claim 2 wherein the second cluster model comprises a cluster model for which a second 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 the 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;

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 selected from the group consisting of a less than an entire length of the first work of literature, and a value less than a chapter or less than a section of the first work of literature, and wherein the second control parameter limiting the range of significance value variation comprises a value selected from the group consisting of a local similarity concept cluster, a regional similarity concept cluster, and a consistency trend cluster extending longitudinally within the index range of the work of literature.

7. A computer program product for comparing and optionally recommending works of digital literature, comprising:

a tangible, computer readable memory storage device;

program instructions stored by the computer readable memory storage device for causing a processor to, when executed, perform steps of;

perform deep semantic analysis to create at least a first digital cluster model for at least a first work of digital literature, wherein the deep semantic analysis comprises machine learning by the computing platform, and wherein the cluster model contains clusters which are limited within a range of significance values for one or more instances within one or more literary element categories;

determine a degree of similarity between the first digital cluster model for a first work of digital literature and a second digital cluster model for a second work of digital literature; and

produce, via a user interface device, a recommendation to a user regarding the degree of similarity;

wherein the first and second cluster models one or more clusters selected from the group consisting of abstracted concepts, abstracted longitudinal patterns, and abstracted consistency trends of elements in the clusters across multiple segments of each respective work of digital literature, wherein the abstracted concepts, abstracted relationships, abstracted longitudinal patterns, and abstracted consistency trends of elements were abstracted from actual elements in the respective works of digital literature by deep semantic analysis.

8. The computer program product as set forth in claim 7 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.

9. The computer program product as set forth in claim 8 wherein the second cluster model comprises a cluster model for which the first user has indicated at least one literary element category to be of interest or disinterest.

10. The computer program product as set forth in claim 8 wherein the second cluster model comprises a cluster model for which a second user has indicated at least one literary element category to be of interest or disinterest.

11. The computer program product as set forth in claim 7 wherein the program instructions for accessing of a first cluster model further comprises program instructions to, when executed, cause a processor to:

receive 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 the 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;

for each literary element category and for each instance within each literary element category of the first work of literature, invoke 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;

receive from the cluster finding process one or more clusters found around one or more instances of one or more literary element categories; and

store the one or more clusters into the first cluster model for the first work of literature.

12. The computer program product as set forth in claim 11 wherein the first control parameter limiting the range of index value variation is set to a value selected from the group consisting of a less than an entire length of the first work of literature, and a value less than a chapter or less than a section of the first work of literature, and wherein the second control parameter limiting the range of significance value variation comprises a value selected from the group consisting of a local similarity concept cluster, a regional similarity concept cluster, and a consistency trend cluster extending longitudinally within the index range of the work of literature.

13. A system for comparing and optionally recommending works of digital literature, comprising:

a computer processor;

a tangible, computer readable memory storage device accessible by the computer processor;

program instructions stored by the computer readable memory storage device for causing the computer processor to, when executed, perform steps of;

perform deep semantic analysis to create at least a first digital cluster model for at least a first work of digital literature, wherein the deep semantic analysis comprises machine learning by the computing platform, and wherein the cluster model contains clusters which are limited within a range of significance values for one or more instances within one or more literary element categories;

determine a degree of similarity between the first digital cluster model for a first work of digital literature and a second digital cluster model for a second work of digital literature; and

produce, via a user interface device, a recommendation to a user regarding the degree of similarity;

wherein the first and second cluster models one or more clusters selected from the group consisting of abstracted concepts, abstracted longitudinal patterns, and abstracted consistency trends of elements in the clusters across multiple segments of each respective work of digital literature, wherein the abstracted concepts, abstracted relationships, abstracted longitudinal patterns, and abstracted consistency trends of elements were abstracted from actual elements in the respective works of digital literature by deep semantic analysis.

14. The system as set forth in claim 13 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.

15. The system as set forth in claim 14 wherein the second cluster model comprises a cluster model for which the first user has indicated at least one literary element category to be of interest or disinterest.

16. The system as set forth in claim 14 wherein the second cluster model comprises a cluster model for which a second user has indicated at least one literary element category to be of interest or disinterest.

17. The system as set forth in claim 13 wherein the program instructions for accessing of a first cluster model further comprises program instructions to, when executed, cause the computer processor to:

receive 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 the 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;

for each literary element category and for each instance within each literary element category of the first work of literature, invoke 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;

receive from the cluster finding process one or more clusters found around one or more instances of one or more literary element categories; and

store the one or more clusters into the first cluster model for the first work of literature.

18. The system as set forth in claim 17 wherein the first control parameter limiting the range of index value variation is set to a value selected from the group consisting of a less than an entire length of the first work of literature, and a value less than a chapter or less than a section of the first work of literature, and wherein the second control parameter limiting the range of significance value variation comprises a value selected from the group consisting of a local similarity concept cluster, a regional similarity concept cluster, and a consistency trend cluster extending longitudinally within the index range of the work of literature.