Recommendation engine using inferred deep similarities for works of literature

• US 9,298,802 B2
• Filed: 12/03/2013
• Issued: 03/29/2016
• 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:
  US14/094,934
• Publication Number:
  US 20150154246A1
• Time in Patent Office: 847 Days
• US Class Current: 1/1
• 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 computer program product for comparing and optionally recommending works of 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;

access a first cluster model for a first work of literature;

access at least a second cluster model for at least a second work of literature;

compare the first and at least second cluster models for similarities;

generate a score according to degrees of match between the first and second cluster models; and

produce 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 patterns or consistency trends of elements in the clusters across multiple segments of each respective work of literature.

2. The computer program product 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.

3. The computer program product as set forth in claim 1 wherein the computer instructions to access a first cluster model comprise instructions for:

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

storing in a computer readable memory device the one or more clusters into the first cluster model for the first work of literature.

4. A system for analyzing digital works of literature and optionally recommending works of literature, comprising:

a computing platform having a processor and one or more tangible computer readable memory storage devices;

a plurality of program instructions stored by the one or more computer readable memory storage devices for causing the processor to;

access a first cluster model for a first work of literature;

access at least a second cluster model for at least a second work of literature;

compare the first and at least second cluster models for similarities;

generate a score according to degrees of match between the first and second cluster models; and

produce 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 patterns or consistency trends of elements in the clusters across multiple segments of each respective work of literature.

5. The system as set forth in claim 4 wherein the program instructions for causing a processor to generate the first cluster model comprise instructions for:

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

storing in a computer readable memory device the one or more clusters into the first cluster model for the first work of literature.