US-12621527-B2 - Re-consuming content suggestions

Abstract

Embodiments determine a user who is watching current content, receive content history of the user, calculate a consumption score (CS) based on series time, watch time, and consumption time of the content history, calculate a time to forget (TTF) threshold value based on the CS, the series time, and days since last viewed of the content history, compare the CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user, and provide at least one suggestion to re-watch content of the content history based on the comparing.

Inventors

• Connor Paul
• Harry Hoots
• Panav Setia
• Jana H. Jenkins

Assignees

• INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date

20260505

Application Date

20230825

Claims (20)

1. 1 . A computer-implemented method, comprising: determining, by a processor set, a user who is watching current content; receiving, by the processor set, content history of the user; calculating, by the processor set, a consumption score (CS) based on series time, watch time, and consumption time of the content history; adjusting, by the processor set, the CS using an artificial intelligence (AI) model based on the user watching a specific series of the current content during a predetermined time period and the user not watching any other content during the predetermined time period; calculating, by the processor set, a time to forget (TTF) threshold value based on the adjusted CS, the series time, and days since last viewed of the content history; comparing, by the processor set, the adjusted CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user; and providing, by the processor set, at least one suggestion to re-watch content of the content history based on the comparing.
2. 2 . The computer-implemented method of claim 1 , wherein the determining the user who is watching the current content comprises analyzing viewing habits of the user to determine the user who is watching the current content, and the CS is further calculated based on √(Series Time)/(Series Time√(Consumption Time/Watch Time)).
3. 3 . The computer-implemented method of claim 1 , wherein the determining the user who is watching the current content further comprises analyzing a remote control interaction from the user to determine the user who is watching the current content, and the TTF threshold value is further calculated based on (CS*Series Time){circumflex over ( )}(√(Days since Last Viewed/Series Time)).
4. 4 . The computer-implemented method of claim 1 , further comprising: receiving tags of the content history; comparing at least one tag of the tags of the content history with at least one tag of the current content; and matching the at least one tag of the content history with the at least one tag of the current content to provide the at least one suggestion, wherein the tags comprise a genre, author, description, and duration of the content history.
5. 5 . The computer-implemented method of claim 1 , wherein the comparing the CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user comprises: determining that the CS is greater than a first predetermined value; determining that the TTF threshold value is greater than a second predetermined value; and determining that the at least one suggestion has a same genre as the current content watched by the user.
6. 6 . The computer-implemented method of claim 1 , wherein the comparing the CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user comprises: determining that the CS is greater than a first predetermined value; determining that the TTF threshold value is greater than a second predetermined value; and determining that the at least one suggestion has a same author as the current content watched by the user.
7. 7 . The computer-implemented method of claim 1 , wherein the content history comprises previously viewed content by the user.
8. 8 . The computer-implemented method of claim 1 , wherein the content history and the current content comprise a series with a plurality of episodes.
9. 9 . The computer-implemented method of claim 1 , further comprising adjusting the TTF threshold value using the AI model based on the user watching the specific series of the current content during the predetermined time period and the user not watching any other content during the predetermined time period.
10. 10 . The computer-implemented method of claim 1 , further comprising adjusting the TTF threshold value using an artificial intelligence (AI) model based on a new season of a specific series of the current content being released at a predetermined time period from a current date.
11. 11 . The computer-implemented method of claim 10 , further comprising adjusting the CS using the AI model based on the new season of the specific series of the current content being released at the predetermined time period from the current date.
12. 12 . A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: monitor current content being consumed by a user, user content preferences, and user content habits; calculate a consumption score (CS) and a time to forget (TTF) threshold value of previously viewed content of the user; adjust the CS using an artificial intelligence (AI) model based on the current content being consumed, the user content preferences, and the user content habits; adjust the TTF threshold value using the AI model based on the current content being consumed, the user content preferences, and the user content habits; compare the adjusted CS and the adjusted TTF threshold value to the user content habits; and sending at least one suggestion to re-watch previously viewed content based on the comparing.
13. 13 . The computer program product of claim 12 , wherein the previously viewed content and the current content being consumed by the user comprise a series with a plurality of episodes, and the CS is further calculated based on a predefined function of series time, consumption time, and watch time.
14. 14 . The computer program product of claim 12 , wherein the previously viewed content and the current content being consumed by the user comprise a movie with a plurality of chapters, and the TTF threshold value is further calculated based on the CS, series time, and days since last viewed.
15. 15 . The computer program product of claim 12 , wherein the adjusting the CS using the AI model based on the current content being consumed, the user content preferences, and the user content habits comprises adjusting the CS using the AI model based on the user watching a specific series of the current content during a predetermined time period the user not watching any other content during the predetermined time period.
16. 16 . The computer program product of claim 12 , wherein the adjusting the TTF threshold value using the AI model based on the current content being consumed, the user content preferences, and the user content habits comprises adjusting the TTF threshold value using the AI model based on the user watching a specific series of the current content during a predetermined time period the user not watching any other content during the predetermined time period.
17. 17 . The computer program product of claim 12 , wherein the adjusting the CS using the AI model based on the current content being consumed, the user content preferences, and the user content habits comprises adjusting the CS using the AI model based on a new season of a specific series of the current content being released at a predetermined time period from a current date.
18. 18 . The computer program product of claim 12 , wherein the adjusting the TTF threshold value score using the AI model based on the current content being consumed, the user content preferences, and the user content habits further comprises adjusting the TTH threshold value using the AI model based on a new season of a specific series of the current content being released at a predetermined time period from a current date.
19. 19 . A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: determine a user who is watching current content; receive content history of the user; calculate a consumption score (CS) based on series time, watch time, and consumption time of the content history; adjust the CS using an artificial intelligence (AI) model based on the user watching a specific series of the current content during a predetermined time period and the user not watching any other content during the predetermined time period; calculate a time to forget (TTF) threshold value based on the adjusted CS, the series time, and days since last viewed of the content history; compare the adjusted CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user; and provide at least one suggestion to re-watch content of the content history based on the comparing, wherein the content history is previously viewed content by the user.
20. 20 . The system of claim 19 , wherein the CS is further calculated based on √(Series Time)/√(Series Time*(Consumption Time/Watch Time)), and the TTF threshold value is further calculated based on (CS*Series Time){circumflex over ( )}(√(Days since Last Viewed/Series Time)).

Description

BACKGROUND Aspects of the present invention relate generally to re-consuming content suggestions and, more particularly, to calculating re-watched content suggestions on a streaming platform. Content streaming services have become a popular way for viewers to enjoy and consume favorite movies and shows. Content streaming refers to media content being delivered to computers and mobile devices via the Internet and played back in real time. SUMMARY In a first aspect of the invention, there is a computer-implemented method including: determining, by a processor set, a user who is watching current content; receiving, by the processor set, content history of the user; calculating, by the processor set, a consumption score (CS) based on series time, watch time, and consumption time of the content history; calculating, by the processor set, a time to forget (TTF) threshold value based on the CS, the series time, and days since last viewed of the content history; comparing, by the processor set, the CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user; and providing, by the processor set, at least one suggestion to re-watch content of the content history based on the comparing. In another aspect of the invention, there is a computer program product including one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to: monitor current content being consumed by a user, user content preferences, and user content habits; calculate a consumption score (CS) and a time to forget (TTF) threshold value of previously viewed content; adjust the CS using an artificial intelligence (AI) model based on the current content being consumed, the user content preferences, and the user content habits; adjust the TTF threshold value using the AI model based on the current content being consumed, the user content preferences, and the user content habits; compare adjusted CS and the adjusted TTF threshold value to the user content habits; and sending at least one suggestion to re-watch previously viewed content based on the comparing. In another aspect of the invention, there is a system including a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to: determine a user who is watching current content; receive content history of the user; calculate a consumption score (CS) based on series time, watch time, and consumption time of the content history; calculate a time to forget (TTF) threshold value based on the CS, the series time, and days since last viewed of the content history; compare the CS and the TTF threshold value to the current content watched by the user and current viewing habits of the user; and provide at least one suggestion to re-watch content of the content history based on the comparing. The content history is previously viewed content by the user. BRIEF DESCRIPTION OF THE DRAWINGS Aspects of the present invention are described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary embodiments of the present invention. FIG. 1 depicts a computing environment according to an embodiment of the present invention. FIG. 2 shows a block diagram of an exemplary environment in accordance with aspects of the present invention. FIG. 3 shows a flowchart of an exemplary method in accordance with aspects of the present invention. FIG. 4 shows a flowchart of another exemplary method in accordance with aspects of the present invention. FIG. 5 shows a flowchart of another exemplary method in accordance with aspects of the present invention. FIG. 6 shows a flowchart of another exemplary method in accordance with aspects of the present invention. DETAILED DESCRIPTION Aspects of the present invention relate generally to re-consuming content suggestions and, more particularly, to calculating re-watched content suggestions on a streaming platform. Embodiments of the present invention allow for re-consuming content by considering past viewing habits of a user, comparing the past viewing habits of the user with current viewing habits of the user, and suggesting content for the user to re-watch that corresponds with at least one current content interest. Embodiments of the present invention analyze past consumed content, monitor current consumed content, compare the past consumed content with the current consumed content, and present suggestions for content re-consumption to the user. Embodiments of the present invention utilize a consumption score (CS) for consumed content, correlate the CS with current viewing habits of a user, and suggest re-consumption of content. In particular, embodiments of the present invention incorporate a speed