TREND IDENTIFICATION AND MODIFICATION RECOMMENDATIONS BASED ON INFLUENCER MEDIA CONTENT ANALYSIS

• US 20190311416A1
• Filed: 04/10/2018
• Published: 10/10/2019
• Est. Priority Date: 04/10/2018
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: PANDIT, Sushain, WANG, Fang, EKAMBARAM, Vijay, RAKSHIT, Sarbajit K.
• Application Number:
  US15/949,071
• Publication Number:
  US 20190311416A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Metadata of influencer media content from content platforms are analyzed, a potential product is identified, and attributes for the potential product is extracted. Profile data of followers of the influencer is obtained, and the followers are clustered. An influence factor of the influencer is calculated for each cluster. The followers in the clusters are ranked based on interactions with the influencer. A potential media content related to the potential product is identified, and a placement recommendation to a given cluster is provided based on the influence factors for the clusters and on the follower ranks. Potential future trends are identified based on information related the influencer and are thus predictive and forward-looking, instead of reactive and backward-looking. The potential media contents and the strategic placement of the potential media contents leverages the anticipation of a trend due to the activities of the influencer.

Claims

1-6. -6. (canceled)

7. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

analyze metadata of at least one media content of an influencer from at least one content platform;

identify at least one potential product from the analysis of the metadata of the media content;

extract a set of attributes for the potential product;

obtain profile data of a plurality of followers of the influencer on the content platform;

cluster the plurality of followers into a plurality of clusters based at least on geographic locations of the plurality of followers;

calculate an influence factor of the influencer for each of the plurality of clusters;

rank the plurality of followers in the plurality of clusters based on follower interactions with the influencer on the content platform;

identify at least one potential media content related to the potential product; and

provide a recommendation of placement of the potential media content to a given cluster of the plurality of clusters based on the influence factor for each of the plurality of clusters and on the ranking of the plurality of followers in the plurality of clusters.

8. The computer program product of claim 7, wherein in the providing of the recommendation of the placement of the potential media content comprises:

calculate a composite score for each of the plurality of clusters from the influence factor and the rankings for the plurality of followers in each of the plurality of clusters;

rank the plurality of clusters based on the composite score; and

generate the recommendation of the placement of the potential media content based on the ranking of the plurality of clusters.

9. The computer program product of claim 7, further causing the processor to:

obtain a set of attributes of each of a plurality of user products from a user device;

compare the set of attributes of the potential product with the set of attributes of each of the plurality of user products;

calculate a similarity index for each of the plurality of user products based on a difference between the set of attributes of the potential product and the set of the attributes of each of the plurality of user products;

generate a plurality of product modification recommendations for the plurality of user products based on the similarity index for each of the plurality of user products; and

provide the plurality of product modification recommendations to a user device.

10. The computer program product of claim 9, wherein the providing of the plurality of product modification recommendations to the user device comprises:

rank the plurality of product modification recommendations based on a set of user preferences from the user device; and

provide a set of ranked product modification recommendations to the user device.

11. The computer program product of claim 9, further causing the processor to:

obtain a description of a plurality of target followers for a given user product associated with a given product modification recommendation of the plurality of product modification recommendations;

match the description of the plurality of target followers with at least one of the plurality of clusters based at least on geographic location associated with the plurality of target followers and the plurality of clusters; and

calculate an impact prediction score for the given product modification recommendation based on the influence factor of the influencer for the at least one of the plurality of clusters matching the description of the plurality of target followers.

12. The computer program product of claim 11, further causing the processor to:

capture a plurality of interactions of the plurality of target followers with a modified user product, wherein the modified user product comprises the given user product has been modified according to the given product modification recommendation;

calculate an actual impact score for the modified user product based on the plurality of interactions;

compare the impact prediction score for the given product modification recommendation with the actual impact score for the modified user product;

calculate a difference between the impact prediction score for the given product modification recommendation and the actual impact score for the modified user product; and

adjust a process for calculation of the impact prediction score based on the difference between the impact prediction score for the given product modification recommendation and the actual impact score for the modified user product.

13. A system comprising:

a processor; and

a computer readable storage medium having program instructions embodied therewith, the program instructions executable by the processor to cause the processor to;

analyze metadata of at least one media content of an influencer from at least one content platform;

identify at least one potential product from the analysis of the metadata of the media content;

extract a set of attributes for the potential product;

obtain profile data of a plurality of followers of the influencer on the content platform;

cluster the plurality of followers into a plurality of clusters based at least on geographic locations of the plurality of followers;

calculate an influence factor of the influencer for each of the plurality of clusters;

rank the plurality of followers in the plurality of clusters based on follower interactions with the influencer on the content platform;

identify at least one potential media content related to the potential product; and

provide a recommendation of placement of the potential media content to a given cluster of the plurality of clusters based on the influence factor for each of the plurality of clusters and on the ranking of the plurality of followers in the plurality of clusters.

14. The system of claim 13, wherein in the providing of the recommendation of the placement of the potential media content comprises:

calculate a composite score for each of the plurality of clusters from the influence factor and the rankings for the plurality of followers in each of the plurality of clusters;

rank the plurality of clusters based on the composite score; and

generate the recommendation of the placement of the potential media content based on the ranking of the plurality of clusters.

15. The system of claim 13, further causing the processor to:

obtain a set of attributes of each of a plurality of user products from a user device;

compare the set of attributes of the potential product with the set of attributes of each of the plurality of user products;

calculate a similarity index for each of the plurality of user products based on a difference between the set of attributes of the potential product and the set of the attributes of each of the plurality of user products;

generate a plurality of product modification recommendations for the plurality of user products based on the similarity index for each of the plurality of user products; and

provide the plurality of product modification recommendations to a user device.

16. The system of claim 15, wherein the providing of the plurality of product modification recommendations to the user device comprises:

rank the plurality of product modification recommendations based on a set of user preferences from the user device; and

provide a set of ranked product modification recommendations to the user device.

17. The system of claim 15, further causing the processor to:

obtain a description of a plurality of target followers for a given user product associated with a given product modification recommendation of the plurality of product modification recommendations;

match the description of the plurality of target followers with at least one of the plurality of clusters based at least on geographic location associated with the plurality of target followers and the plurality of clusters; and

calculate an impact prediction score for the given product modification recommendation based on the influence factor of the influencer for the at least one of the plurality of clusters matching the description of the plurality of target followers.

18. The system of claim 17, further causing the processor to:

capture a plurality of interactions of the plurality of target followers with a modified user product, wherein the modified user product comprises the given user product has been modified according to the given product modification recommendation;

calculate an actual impact score for the modified user product based on the plurality of interactions;

compare the impact prediction score for the given product modification recommendation with the actual impact score for the modified user product;

calculate a difference between the impact prediction score for the given product modification recommendation and the actual impact score for the modified user product; and

adjust a process for calculation of the impact prediction score based on the difference between the impact prediction score for the given product modification recommendation and the actual impact score for the modified user product.