AUTOMATIC ARTIST AND CONTENT BREAKOUT PREDICTION

• US 20170024650A1
• Filed: 07/21/2016
• Published: 01/26/2017
• Est. Priority Date: 07/24/2015
• Status: Active Grant

• Current Assignee: Spotify AB (Spotify Technology SA)
• Original Assignee: Spotify AB (Spotify Technology SA)
• Inventors: Golightly, Michael D., Jacobson, Kurt
• Granted Patent: US 9,934,467 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/489 using time information
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/986 Document structures and storage, e.g. HTML extensions
• CPC Class Codes: G06F 40/221 Parsing markup language streams streaming G06F40/149
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/06 specially adapted for file transfer, e.g. file transfer protocol [FTP]

Abstract

Methods, systems and computer program products for predicting breakout artists and media content. Web content is scraped to identify web pages that include the name of an artist or media item and clusters the web pages into one or more headlines. The number of headlines for the artist or media item that occurred during a first time period and the number of content consumers who played the artist or media item during the first time period and during a second time period are counted. A breakout value is, in turn, calculated for the artist or media item using the number of headlines over the first time period, the number of content consumers during the first time period and the number of content consumers during the second time period.

Claims

1. A method of predicting breakout content, comprising the steps of:

scraping a network for pages that include a name of an entity;

clustering the pages into one or more headline clusters;

counting a number of the headline clusters over a first time period;

counting a number of content consumers for the entity over the first time period and a number of content consumers for the entity over a second time period; and

calculating a value using the number of headline clusters over the first time period, the number of content consumers over the first time period and the number of content consumers over the second time period.

2. The method according to claim 1, wherein the value is calculated according to:

3. The method according to claim 1, wherein the second time period is greater than the first time period.

4. The methods according to claim 3, wherein the first time period is equal to 7 days and the second time period is equal to 6 months.

5. A system comprising for predicting breakout content, comprising:

a processing unit adapted to

scraping a network for pages that include a name of an entity;

clustering the pages into one or more headline clusters;

count a number of the headline clusters over a first time period;

count a number of content consumers for the entity over the first time period and a number of content consumers for the entity over a second time period; and

calculate a value using the number of headline clusters over the first time period, the number of content consumers over the first time period and the number of content consumers over the second time period.

6. The system according to claim 5, wherein the value is calculated according to:

7. The system according to claim 5, wherein the second time period is greater than the first time period.

8. The system according to claim 7, wherein the first time period is equal to 7 days and the second time period is equal to 6 months.

9. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by an electronic device with one or more processors, cause the electronic device to perform the steps of:

scraping a network for pages that include a name of an entity;

clustering the pages into one or more headline clusters;

counting a number of the headline clusters over a first time period;

counting a number of content consumers for the entity over the first time period and a number of content consumers for the entity over a second time period; and

calculating a value using the number of headline clusters over the first time period, the number of content consumers over the first time period and the number of content consumers over the second time period.

10. The method according to claim 9, wherein the value is calculated according to:

11. The computer-readable medium of claim 10, wherein the second time period is greater than the first time period.

12. The computer-readable medium of claim 11, wherein the first time period is equal to 7 days and the second time period is equal to 6 months.