Social media platform with recommendation engine refresh

Abstract

A computing system is provided that includes one or more processors configured to provide a social media platform configured to provision a content feed to a user. The processor is further configured to generate user content interaction information by detecting a user interaction with the content feed, and provide a recommendation engine that selects a content item for display in the content feed based on the generated user content interaction information. The processor is further configured to receive a refresh request to refresh the recommendation engine and, upon receiving the refresh request, refresh the recommendation engine at least in part by masking or resetting the user content interaction information. After the refresh, the processor is further configured to generate, via the recommendation engine, a refreshed content item based on the masked or reset user content interaction information, and transmit the refreshed content item to the user device for display in the content feed.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application No. 17 / 935,902, filed on September 27, 2022, entitled “SOCIAL MEDIA PLATFORM WITH RECOMMENDATION ENGINE REFRESH,” the entire contents of which are incorporated herein by reference. Background Art

[0003] With the rise of online social media platforms that allow original media to be shared with the world in seconds, users upload, consume, and engage with a large amount of content every day. This content includes text posts, photos, long videos, and short videos. However, due to the huge amount of content on social media platforms, it is difficult for users to find uploaded content that suits their taste. A recommendation system has been developed to address this problem, which recommends content to be displayed in the content feed for each user. The recommendation system based on artificial intelligence (AI) models recommends content to users based on their previous interactions with the content on the social media platform, thereby effectively curating each user's content feed on the social media platform. Summary of the invention

[0004] In order to solve the problems discussed herein, a computerized system and method are provided. On the one hand, a computerized system is provided, the system including one or more processors, the processor being configured to execute instructions stored in a memory to provide a social media platform, the social media platform being configured to supply content feeds to a user computing device of a user. The processor is also configured to generate user content interaction information by detecting user interactions with the content feed, and to provide a recommendation engine for selecting content items for display in the content feed based on the generated user content interaction information. The processor is also configured to receive a refresh request for refreshing the recommendation engine via a graphical user interface (GUI) including a refresh selector, and in response to receiving the refresh request, refresh the recommendation engine at least in part by masking or resetting the user content interaction information. After refreshing, the processor is also configured to input the masked or reset user content interaction information into the recommendation engine. The processor is also configured to generate refreshed content items via the recommendation engine based on the masked or reset user content interaction information, and transmit the refreshed content items to the user device for display in the content feed.

[0005] This Summary is intended to introduce some concepts in a simplified form, which will be further described in the Detailed Description below. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 A schematic diagram of a computing system including a social media platform configured to supply a content feed to a user computing device, wherein content items in the content feed are selected based on user content interaction information and a recommendation engine is refreshed by masking or resetting the user content interaction information.

[0007] Figure 2 Shown according to Figure 1 An example graphical user interface (GUI) of a user computing device of a system of FIG. 1 that displays a content feed.

[0008] Figure 3A and Figure 3B Shown according to Figure 1 An example GUI for a system of FIG. 1 that displays a content feed refresh selector on a user computing device.

[0009] Figure 4 shows example embeddings generated by the embedding generator and by Figure 1 Schematic diagram of the masked embedding generated by the masking module of the system.

[0010] Figure 5 shows that after the recommendation engine refresh request, Figure 1 Example masking rules implemented by a masking module of a system.

[0011] Figure 6 Shown according to Figure 1 An example GUI for a system that displays a content feed on a user computing device before and after a recommendation engine refresh.

[0012] Figure 7 A flow chart of a computerized method for refreshing a recommendation engine according to an example implementation of the present disclosure is shown.

[0013] Figure 8 An example computing environment is shown according to which embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0014] As discussed above, computer-based techniques have been developed to enhance the user experience on social media platforms, where artificial intelligence systems detect and utilize users' interactions with content to determine their tastes and preferences. The system uses this data to select content for the user, allowing the user to enjoy personalized media content selected in a personalized manner from the multitude of media content available on the platform. However, in the event that the user's interests change, the recommendation system may select content that no longer reflects the user's taste based on their previous content interactions. As a result, the recommendation system will supply the user with content that the user is no longer interested in. This can result in a reduction in the user's enjoyment of the social media platform.

[0015] In view of the problems discussed above, a social media platform refreshed by a recommendation engine is provided. Figure 1 A schematic diagram of a computing system 2 including a social media platform 16 is shown, the social media platform 16 being configured to supply a content feed 10 to a user computing device 38 of a user, wherein content items 12 in the content feed 10 are selected via a recommendation engine 8 based on content interaction information 24, and the recommendation engine 8 is refreshed by masking or resetting the user content interaction information 24. The computing system 2 may include one or more processors 4 configured to execute instructions using an associated memory 6 to perform the functions and processes of the computing system 2 described herein. For example, the computing system 2 may include a cloud server platform including a plurality of server devices, and the one or more processors 4 may be one processor of a single server device or multiple processors of a plurality of server devices. Below, the functions of the computing system 2 performed by the processor 4 are described by way of example, and the description should be understood to include execution on one or more processors 4 distributed among one or more devices discussed above.

[0016] The social media platform is configured to generate a personalized content feed 10 for each user based on user and content data 18, the user and content data 18 including user information 20, the user information 20 including user identification (ID) 22, user content interaction information 24, content information 26, device information 28, search history 32, and recommendation history information 34; and is configured to supply the content feed 10 to the user's user computing device 38. The user and content data 18 are also used to personalize the user's experience of other services 60 (such as advertising). For example, the user information 20 includes a unique user identification 22, a password, the user's language, country, gender, and interest categories selected by the user when creating an account. The content information 26 includes features of the content, such as keywords in the subtitles, topic tags, and audio content identification. For example, the content information 26 can identify a video as being related to cars, travel, cooking, or various other topics based on keywords present in the subtitles or based on topic tags. In addition, the content information 26 can include the number of views or likes that the content item has received. In addition, the content information 26 may indicate whether the content uses a specific image or video filter, template, etc. The content information does not include user-specific information, but rather includes information about content (such as videos, images, and text uploaded by the user and stored on the social media platform). The device information 28 includes information about the user computing device 38, such as the location, device type, operating system, and time zone of the user computing device 38. This information can be updated each time the user computing device communicates with the social media platform. The computing device 38 can be any type of various computing devices, such as smart phones, tablet computing devices, head-mounted display devices, notebook computers, desktops, smart watches, etc. The search history 32 includes keywords entered by the user as search queries in the search tool of the social media platform. The recommendation history 34 includes content items 12 previously recommended by the computing system 2 that appear in the content feed 10.

[0017] The user content interaction information 24 is generated by detecting user interactions with the content feed 10 via the processor 4 of the system 2. The processor 4 is configured to detect user interactions with content items 12 in the content feed 10, record the user's interactions as the user content interaction information 24, and update the information as the user views and interacts with the content items 12 by sending data indicating updates 47 from the user's computing device 38 to the computing system 2. The content items 12 can be any of a variety of digital content types, such as video, audio, or images. For example, the user content interaction information 24 can include other accounts followed by the user, content that the user likes or shares, content that the user comments on, content that the user adds to his or her favorites, and content that the user marks as "not interested".

[0018] The processor 4 is also configured to provide a recommendation engine 8 and input the generated user content interaction information 24 to the recommendation engine 8. The recommendation engine 8 may include a trained machine learning model 8A and a masking module 48, the masking module 48 being configured to selectively mask the input of the trained machine learning model 8A based on instructions from the refresh request handler 45. The trained machine learning model 8A may be trained to predict content items with which a user may interact based on the user's previous interactions with the social media platform. Via the trained machine learning model 8A, the recommendation engine 8 selects content items 12 for display in the content feed 10 based on the generated user content interaction information 24. The trained machine learning model 8A of the recommendation engine 8 may be built on a trained neural network such as a Transformer model, which is a deep learning model that employs a self-attention mechanism to weight the importance of each part of the input data differently. In addition to the user content interaction information 24, the user information 20, the content information 26, and the device information 28 may be input into the trained machine learning model 8A of the recommendation engine 8 and used by the engine to select content items 12 for display in the content feed 10. For example, a user looking for a car to buy may follow a car dealer's account, view videos uploaded by the car dealer featuring cars for sale, and comment on the videos. In this case, the trained machine learning model 8A of the recommendation engine 8 may select content items 12 featuring cars for sale to appear in the user's content feed 10. In addition, if the user information 20 indicates that the user lives in the United States, the trained machine learning model 8A of the recommendation engine 8 may be trained to select content items 12 showing cars for sale in the United States.

[0019] Briefly go to Figure 2 , illustrates an example GUI 40 displaying the content feed 10 on the user computing device 38. The processor 4 is also configured to supply a graphical user interface (GUI) 40 to the user computing device 38, the graphical user interface 40 being configured to display the content feed 10. Figure 2, the GUI 40 of the content feed 10 includes content items 12, a "like" icon 80, a "comment" icon 82, a "favorite" icon 84, and a settings icon 86 for the user to view and enter user account settings. In this example, the content items 12 including cars (selected by the trained machine learning model 8A of the recommendation engine 8) are displayed on the content feed 10. The "like" icon 80 takes the form of a heart symbol, and the user can use the like button by clicking on the "like" icon 80 for the user's favorite content item 12. The "comment" icon 82 allows the user to add a comment to the content item 12. The "favorite" icon 84 allows the user to add the content item 12 to the user's favorites for easy access later. Clicking these icons is an interaction between the user and the content item 22, and this interaction is detected and stored as the user content interaction information 24 discussed above. The GUI 40 also includes a settings icon 86, which allows the user to manage the user's account settings for the purposes discussed herein.

[0020] GUI 40 also includes a refresh selector 42. Upon selection of refresh selector 42, user computing device 38 is configured to send a refresh request to computing system 2. Refresh request handler 45 executed by processor 4 of computing system 2 is configured to receive a refresh request 44 from user computing device 38 to refresh recommendation engine 8 upon user selection of refresh selector 42. Refresh request handler 45 may be configured to communicate with masking module 48 of recommendation engine 8 to instruct the masking module to perform masking according to masking rules 50 to implement the requested refresh operation. Figure 3A and Figure 3B , illustrates an example GUI 40 showing a recommendation engine refresh selector 42 on a user computing device 38. Figure 3A As shown in Figure 2 When the settings icon 86 is selected, the GUI 40 displays the account settings 90, which enables the user to manage settings such as security, privacy, and content feed preferences 92. Figure 3B As shown in , when the user selects the content feed preference 92, the GUI 40 displays a recommendation engine refresh selector 42, which enables the user to issue a refresh request to refresh the recommendation engine 8. When the refresh request is issued, the GUI 40 can display text that explains the meaning of the recommendation engine refresh and the impact of activating such a recommendation engine refresh on the user experience. The text can also prompt the user to "cancel" or "confirm" the refresh. Selecting the "confirm" option activates the recommendation engine refresh. In addition, the processor 4 can be configured to receive a request from the user to cancel the recommendation engine refresh, and in response, execute the request to cancel the recommendation engine refresh. This request can be issued when the user is not satisfied with the refreshed content items after the recommendation engine refresh.

[0021] Back to Figure 1 In response to receiving the refresh request 44 as discussed above, the processor 4 is configured to refresh the recommendation engine 8 at least in part by masking or resetting the user content interaction information 24. After receiving the request, the processor 4 is configured to mask a portion of the user and content data 18 (including the user information 20 and the user content interaction information 24) via the masking module 48 according to the masking rule 50. The processor 4 is also configured to input the masked or reset user content interaction information 24 and user information 20 to the trained machine learning model 8A of the recommendation engine 8. Masking the user content interaction information 24 and user information 20 may include masking the original data of the user content interaction information 24 and user information 20 and masking the embedding corresponding to the user content interaction information 24 and user information 20.

[0022] Briefly go to Figure 4, illustrates a schematic diagram of the process of embedding generator 112 generating embedding and masking module 48 masking the generated embedding. Initially, feature identification (ID) 102 is extracted from the user's original data 100 and the user and content data 18 including user content interaction information 24 via feature extraction module 110. Each feature ID 102 uniquely identifies the corresponding feature within the data set. For example, the feature ID 102 extracted from the user information 20 may include the United States as the user's country and English as the user's language. The feature ID extracted from the user content interaction information 24 may include content items that the user has liked and commented on. After the feature ID 102 is extracted, an embedding 104 of the feature ID 102 is generated via the embedding generator 112. The embedding 104 is a numerical vector (e.g., [1, 0.5, 2.1 ...]) representing the feature ID 102. The embedding 104 is provided to the masking module 48 to generate a masked embedding 106 for specific target information by setting the vector value to a predetermined masking value (such as zero). Alternatively, masking can be achieved by setting the vector value to other values ​​that cause the recommendation engine 8 to effectively ignore or disregard the masked embedding 106. In the depicted example, embedding 1 (EMB 1) to embedding 100 (EMB 100) representing user information 20 and user content interaction information 24 are masked. These embeddings are referred to as masked embeddings 106. Embeddings 101 (EMB 101) to embedding N (EMB N) representing other information such as content information 26 and device information 28 are unmasked. These embeddings are referred to as unmasked embeddings 107. The masked embeddings 106 are input to the trained machine learning model 8A of the recommendation engine 8 together with the unmasked embeddings 107. The masked embeddings 106 with a predetermined masking value (such as 0) are disregarded by the trained machine learning model 8A of the recommendation engine 8 to select content items. That is, in the depicted example, the trained machine learning model 8A of the recommendation engine 8 does not consider the user information 20 and the user content interaction information 24 when selecting the refreshed content item 12. In addition, because the recommendation engine refresh only masks a portion of the user and content data 18 (such as the user information 20 and the user content interaction information 24), rather than deleting all user-specific data associated with the user account, the user can continue to use the platform's services without setting up a new account. By not masking the device information 28, the user can continue to view content oriented to the user's geographic location, which is an example. In addition, since the user search history 32 is not deleted, after refreshing the recommendation engine 8, the functions of the social media platform (such as a search tool that provides personalized search results) will work in the way the user is accustomed to.In addition, because the user information 20 and the user content interaction information 24 are masked rather than deleted, and the search history 32 and the recommendation history 34 are not deleted, after the recommendation engine is refreshed, sponsored content such as advertisements can be directed to users based on these data sources, thereby enabling more relevant sponsored content to be displayed to each user than if such information is deleted.

[0023] As discussed above, after the recommendation engine refresh, the masking module 48 masks the user information 20 and the user content interaction information 24 according to the masking rules 50 . Figure 5 Shown by Figure 1 2. As shown in 200, no information in the user and content data 18 is masked or reset before a refresh request is issued. According to the masking rules 50, the refresh of the recommendation engine 8 may include a first temporary refresh, which masks or resets the user content interaction information 24 and the user information 20 including the user identification 22 until a first predetermined threshold number of views 50A is reached. In the depicted example, as shown in 202, after the refresh request is issued, the user information 20 including the user identification 22 and the user content interaction information 24 is masked according to the masking rules 50 until the number of views of the content item 12 after the refresh reaches the first predetermined threshold. The first predetermined number of views 50A can be in the range of 25 to 100 views, and in a specific embodiment is 50 views. As shown in 204, after the first predetermined number of views have occurred, the user content interaction information 24 and the user information 20 (excluding the user identification 22) are unmasked, so that only the user identification 22 continues to be masked. Therefore, according to the masking rule 50, the refresh of the recommendation engine may include a second temporary refresh, which unmasks the user content interaction information 24 and the user information 20 (except the user identification 22), and masks the user identification 22 after the first predetermined threshold number of views 50A occurs until the second predetermined threshold number of views 50B is reached. The second predetermined number of views can be between 150 and 300 views, and in a specific embodiment is 200 views. The computing system 2 is also configured to end the second temporary refresh by unmasking the user identification 22 after the second predetermined threshold number of views 50B is reached. Therefore, as shown in 206, after the second predetermined number of views 50B, the user identification 22 is also unmasked, and no other information is masked.

[0024] After performing a recommendation engine refresh, the processor 4 is configured to generate a refreshed content item 12 via the recommendation engine 8 based on the masked or reset user content interaction information 24 and user information 20 and based on the user's device information 28, search history 32 and recommendation history 34 that are not masked or reset, and transmit the refreshed content item 12 to the user computing device 38 for display in the content feed 10. Figure 6 Shown according to Figure 1 2, which displays content feed 10 on user computing device 38 before and after a recommendation engine refresh. Depicted example 250 shows GUI 40 before recommendation engine refresh, wherein recommendation engine 8 includes content items 12 selected for a user by the present invention. In the depicted case, content feed 10 before refresh includes content items 12 featuring cars, as selected by the recommendation engine. Conversely, depicted example 252 shows GUI 40 after recommendation engine refresh, wherein content feed 10 includes content items 12 to be selected for a new user. In the depicted case, after the content refresh, content feed 10 includes content items 12 that are preferred by a majority of users of social media platform 16, rather than content items 12 featuring cars. As the user continues to use the recommendation engine 16 based on Figure 1 When the user's social media platform 16 is updated, the computing system 2 repeats the process of collecting new data, determining the user's new preferences, and selecting appropriate content items 12 to include on the user's content feed 10 so that the feed accurately reflects the user's recent tastes.

[0025] In one configuration of the computing system 2, it should be appreciated that the processor 4 can be configured to mask or reset a portion of the user and content data 18 (such as user information 20 and user content interaction information 24), and provide the masked information to other services 60 (such as advertisements provided by the social media platform 16), enabling the user to refresh the advertising content in the same manner as the content feed 10 discussed above.

[0026] Figure 7 A flowchart of a computerized method 300 for refreshing a recommendation engine according to an example implementation of the present disclosure is shown. The method 300 may be implemented by the hardware and software of the computing system 2 described above, or by other suitable hardware and software. At step 304, the method 300 may include providing a social media platform configured to supply a content feed to a user computing device of a user.

[0027] At step 306, the method may also include generating user content interaction information by detecting user interactions with the content feed. At step 308, the method may also include providing a recommendation engine that selects content items for display in the content feed based on user information including a user identification for the user and the generated user content interaction information. In addition, content information, device information, and other information may be input into the recommendation engine.

[0028] At step 310, the method may further include providing a graphical user interface (GUI) configured to display the content feed to a user device, wherein the GUI includes a refresh selector, a refresh request from the user device upon user selection of the refresh selector.

[0029] At step 312, the method may also include, in response to receiving the refresh request, refreshing the recommendation engine at least in part by masking or resetting the user content interaction information. As indicated at step 314, the user information and the user content interaction information may be masked by generating embedded information representing the user information and the user content interaction information and masking the embedded information.

[0030] At step 316, the method may further include inputting the masked or reset user information and user content interaction information into a recommendation engine. At step 318, the method may further include generating a refreshed content item via the recommendation engine based on the masked or reset user information and user content interaction information. At step 320, the method may further include transmitting the refreshed content item to the user computing device for display in the content feed.

[0031] The systems and methods described above can be implemented to reset a user's content feed on a social media platform without having to recreate a new user account or delete data reflecting the user's interaction with content on the platform. The systems and methods discussed above can produce impactful changes in content feeds that can be quickly perceived by the user without interrupting the user's continued use of the platform.

[0032] In some embodiments, the methods and processes described herein may be associated with a computing system of one or more computing devices. In particular, such methods and processes may be implemented as a computer application or service, an application programming interface (API), a library, and / or other computer program products.

[0033] Figure 8 A non-limiting embodiment of a computing system 600 that can implement one or more of the above methods and processes is schematically illustrated. The computing system 600 is shown in simplified form. The computing system 600 can embody the above and Figure 12. The computing system 600 may take the form of one or more personal computers, server computers, tablet computers, home entertainment computers, network computing devices, gaming devices, mobile computing devices, mobile communication devices (e.g., smart phones), and / or other computing devices, as well as wearable computing devices (such as smart watches and head-mounted augmented reality devices).

[0034] The computing system 600 includes a logic processor 602, a volatile memory 604, and a non-volatile storage device 606. The computing system 600 may optionally include a display subsystem 608, an input subsystem 610, a communication subsystem 612, and / or Figure 1 Other components not shown.

[0035] Logical processor 602 includes one or more physical devices configured to execute instructions. For example, a logical processor may be configured to execute instructions that are part of one or more applications, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform tasks, implement data types, transform the state of one or more components, implement technical effects, or otherwise achieve a desired result.

[0036] The logical processor may include one or more physical processors (hardware) configured to execute software instructions. Additionally or alternatively, the logical processor may include one or more hardware logic circuits or firmware devices configured to execute hardware-implemented logic or firmware instructions. The processor of the logical processor 602 may be single-core or multi-core, and the instructions executed thereon may be configured to be sequential, parallel, and / or distributed processing. Optionally, the various components of the logical processor may be distributed in two or more separate devices, which may be remotely located and / or configured to coordinate processing. Various aspects of the logical processor may be virtualized and executed by a remotely accessible networked computing device configured in a cloud computing configuration. In this case, it will be understood that these virtualized aspects are run on different physical logical processors of various different machines.

[0037] The non-volatile storage device 606 includes one or more physical devices that are configured to store instructions that can be executed by a logical processor to implement the methods and processes described herein. When implementing such methods and processes, the state of the non-volatile storage device 606 may change - for example, to store different data.

[0038] The non-volatile storage device 606 may include a removable and / or built-in physical device. The non-volatile storage device 606 may include an optical memory (e.g., CD, DVD, HD-DVD, Blu-ray disc, etc.), a semiconductor memory (e.g., ROM, EPROM, EEPROM, FLASH memory, etc.), and / or a magnetic memory (e.g., a hard disk drive, a floppy disk drive, a tape drive, MRAM, etc.), or other mass storage device technology. The non-volatile storage device 606 may include a non-volatile, dynamic, static, read / write, read-only, sequential access, location addressable, file addressable, and / or content addressable device. It will be appreciated that the non-volatile storage device 606 is configured to save instructions even when power to the non-volatile storage device 606 is cut off.

[0039] Volatile memory 604 may include physical devices that include random access memory. Volatile memory 604 is typically used by logical processor 602 to temporarily store information during processing software instructions. It will be appreciated that when volatile memory 604 is powered off, volatile memory 604 typically does not continue to store instructions.

[0040] Aspects of the logic processor 602, volatile memory 604, and non-volatile storage device 606 may be integrated together into one or more hardware logic components. For example, such hardware logic components may include field programmable gate arrays (FPGAs), program and application specific integrated circuits (PASIC / ASIC), program and application specific standard products (PSSP / ASSP), systems on chips (SOCs), and complex programmable logic devices (CPLDs).

[0041] The terms "module", "program", and "engine" may be used to describe an aspect of a computing system 600, which is typically implemented in software by a processor to use portions of volatile memory to perform a specific function that involves a transformation process that specifically configures the processor to perform the function. Thus, a module, program, or engine may be instantiated via a logical processor 602 using portions of volatile memory 604 to execute instructions stored by a non-volatile storage device 606. It will be appreciated that different modules, programs, and / or engines may be instantiated from the same application, service, code block, object, library, routine, API, function, etc. Likewise, the same module, program, and / or engine may be instantiated by different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms "module", "program", and "engine" may encompass individual executable files, data files, libraries, drivers, scripts, database records, etc., or groups thereof.

[0042] When included, the display subsystem 608 can be used to present a visual representation of the data stored by the non-volatile storage device 606. The visual representation can take the form of a graphical user interface (GUI). When the methods and processes described herein change the data stored by the non-volatile storage device, thereby transforming the state of the non-volatile storage device, the state of the display subsystem 608 can also be transformed to visually represent the changes in the underlying data. The display subsystem 608 can include one or more display devices utilizing almost any type of technology. Such a display device can be combined with the logical processor 602, the volatile memory 604, and / or the non-volatile storage device 606 in a shared housing, or such a display device can be a peripheral display device.

[0043] When included, the input subsystem 610 may include or interact with one or more user input devices (such as a keyboard, mouse, touch screen, or game controller). In some embodiments, the input subsystem may include or interact with selected natural user input (NUI) component portions. Such component portions may be integrated or peripheral, and the conversion and / or processing of input actions may be performed on-board or off-board. Example NUI component portions may include: microphones for voice and / or speech recognition; infrared, color, stereo, and / or depth cameras for machine vision and / or gesture recognition; head trackers, eye trackers, accelerometers, and / or gyroscopes for motion detection and / or intent recognition; and electric field sensing component portions for assessing brain activity; and / or any other suitable sensors.

[0044] When included, the communication subsystem 612 can be configured to communicatively couple the various computing devices described herein to each other and to communicatively couple to other devices. The communication subsystem 612 can include wired and / or wireless communication devices compatible with one or more different communication protocols. As a non-limiting example, the communication subsystem can be configured to communicate via a wireless telephone network, a wired or wireless local area network, or a wide area network (such as HDMI on a Wi-Fi connection). In some embodiments, the communication subsystem can allow the computing system 600 to send messages to other devices and / or receive messages from other devices via a network such as the Internet.

[0045] The following paragraphs provide additional support for the claims of the subject application. On the one hand, a computing system is provided. The computing system may include one or more processors configured to execute instructions stored in a memory to provide a social media platform configured to supply a content feed to a user computing device of a user. The processor may also be configured to generate user content interaction information by detecting user interactions with the content feed, and to provide a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information. The processor may also be configured to receive a refresh request to refresh the recommendation engine, and in response to receiving the refresh request, refresh the recommendation engine at least in part by masking or resetting the user content interaction information. The processor may also be configured to input the masked or reset user content interaction information into the recommendation engine, generate refreshed content items based on the masked or reset user content interaction information via the recommendation engine, and transmit the refreshed content items to the user computing device for display in the content feed. The processor may also be configured to: provide a graphical user interface (GUI) configured to display the content feed to the user computing device, the GUI including a refresh selector; and receive a refresh request from the user computing device when a user selects the refresh selector.

[0046] According to this aspect, the recommendation engine may select content items for display based on generated user content interaction information and user information, wherein the user information includes at least a user identification for the user. The recommendation engine may also select content items for display based on generated user content interaction information, user information, and device information of the user.

[0047] According to this aspect, the processor may be further configured to: in response to receiving the refresh request, refresh the recommendation engine at least in part by additionally masking or resetting the user information including the user identification.

[0048] According to this aspect, the refreshing of the recommendation engine may include a first temporary refresh that masks or resets the user content interaction information and the user information including the user identification until a first predetermined threshold number of views is reached. The refreshing of the recommendation engine may also include a second temporary refresh that unmasks the user content interaction information and the user information other than the user identification, and masks the user identification after the first predetermined threshold number of views occurs and until a second predetermined threshold number of views is reached.

[0049] According to this aspect, the processor may be further configured to end the second temporary refresh by unmasking the user identification after reaching a second predetermined threshold number of views.

[0050] According to this aspect, the processor may also be configured to generate embedded information based on the user content interaction information, and refresh the recommendation engine by masking or resetting the embedded information representing the user content interaction information.

[0051] According to this aspect, the processor may be further configured to receive a request from a user to cancel a recommendation engine refresh, and cancel the recommendation engine refresh in response to receiving the request.

[0052] According to another aspect of the present disclosure, a computerized method is provided. The computerized method may include providing a social media platform configured to supply a content feed to a user computing device of a user. The computerized method may also include generating user content interaction information by detecting user interactions with the content feed. The computerized method may also include providing a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information. The computerized method may also include receiving a refresh request to refresh the recommendation engine, and in response to receiving the refresh request, refreshing the recommendation engine at least in part by masking or resetting the user content interaction information. The computerized method may also include inputting the masked or reset user content interaction information into the recommendation engine. The computerized method may also include generating a refreshed content item based on the masked or reset user content interaction information via the recommendation engine, and transmitting the refreshed content item to the user computing device for display in the content feed. The computerized method may also include providing a graphical user interface (GUI) configured to display a content feed to a user computing device, the GUI including a refresh selector, and receiving a refresh request from the user computing device when the user selects the refresh selector. The computerized method may also include: selecting content items for display based on the generated user content interaction information and user information via the recommendation engine, wherein the user information includes at least a user identification for the user. The computerized method may also include: in response to receiving the refresh request, refreshing the recommendation engine at least in part by additionally masking or resetting the user information including the user identification. The computerized method may also include: generating embedded information based on the user content interaction information, and masking or resetting the user content interaction information by masking or resetting the embedded information representing the user content interaction information, thereby refreshing the recommendation engine.

[0053] According to this aspect, the recommendation engine refresh may include a first temporary refresh that masks or resets the user content interaction information and the user information including the user identification for a first predetermined threshold number of views. The recommendation engine refresh may also include a second temporary refresh that unmasks the user content interaction information and the user information other than the user identification and masks the user identification for a second predetermined threshold number of views.

[0054] According to another aspect of the present disclosure, a computer-readable medium is provided. The computer-readable medium may include instructions that, when executed by one or more processors, cause the one or more processors to perform the following steps: providing a social media platform configured to supply a content feed to a user computing device of a user; and generating user content interaction information by detecting user interactions with the content feed. These steps may also include providing a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information, user information including at least one user identifier, and device information of the user. These steps may also include receiving a refresh request to refresh the recommendation engine, and in response to receiving the refresh request, refreshing the recommendation engine at least in part by masking or resetting the user content interaction information and the user information. The steps may also include inputting the masked or reset user content interaction information, the masked or reset user information, and the user's device information into the recommendation engine, and generating a refreshed recommended content item based on the masked or reset user content interaction information and the masked or reset user information via the recommendation engine. The steps may also include transmitting the refreshed content item to the user computing device for display in the content feed.

[0055] It should be understood that the configuration and / or method described herein are exemplary in nature, and these specific embodiments or examples should not be considered as restrictive, because many variations may exist. The specific routine or method described herein may represent one or more of any number of processing strategies. Therefore, the various actions illustrated and / or described may be performed in the order illustrated and / or described, in other orders, in parallel, or by omitting them. Equally, the order of the process described above may be changed.

[0056] The subject matter of the present disclosure includes all novel and non-obvious combinations and sub-combinations of the various processes, systems and configurations, and other features, functions, acts, and / or properties disclosed herein, as well as any and all equivalents thereof.

Claims

1. A computing system comprising: one or more processors configured to execute instructions stored in the memory to: providing a social media platform configured to supply a content feed to a user computing device of a user; generating user content interaction information by detecting user interactions with the content feed; providing a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information; receiving a refresh request for refreshing the recommendation engine; as well as In response to receiving the refresh request, the recommendation engine is refreshed at least in part by masking or resetting the user-content interaction information.

2. The computing system of claim 1 , wherein the one or more processors are further configured to: inputting the masked or reset user content interaction information into the recommendation engine; generating, via the recommendation engine, a refreshed content item based on the masked or reset user content interaction information; and The refreshed content item is transmitted to the user computing device for display in the content feed.

3. The computing system of claim 1 , wherein the one or more processors are further configured to: supplying a graphical user interface GUI, the GUI configured to display the content feed to the user computing device, the GUI comprising a refresh selector; and The refresh request is received from the user computing device when a user selects the refresh selector.

4. The computing system of claim 1, wherein The recommendation engine selects the content item for display based on the generated user-content interaction information and user information, wherein the user information includes at least a user identification for the user.

5. The computing system of claim 4, wherein The recommendation engine selects the content item for display based on the generated user-content interaction information, the user information, and the user's device information.

6. The computing system of claim 4, wherein the one or more processors are further configured to, in response to receiving the refresh request, refresh the recommendation engine at least in part by additionally masking or resetting the user information including the user identifier.

7. The computing system of claim 6, wherein the refresh of the recommendation engine comprises a first temporary refresh that masks or resets the user content interaction information and the user information including the user identification until a first predetermined threshold number of views is reached.

8. The computing system of claim 7 , wherein the refresh of the recommendation engine comprises a second interim refresh that unmasks the user content interaction information and the user information other than the user identification, and that masks the user identification after the first predetermined threshold number of views has occurred and until a second predetermined threshold number of views is reached. 9 . The computing system of claim 8 , wherein the one or more processors are further configured to end the second interim refresh by unmasking the user identification after the second predetermined threshold number of views is reached.

10. The computing system of claim 1, wherein the one or more processors are further configured to: generating embedded information based on the user content interaction information; and The recommendation engine is refreshed by masking or resetting the user content interaction information or by masking or resetting the embedded information representing the user content interaction information.

11. The computing system of claim 1 , wherein the one or more processors are further configured to: receiving a request from a user to cancel a refresh of the recommendation engine; and In response to receiving the request, the recommendation engine refresh is overturned.

12. A computerized method for refreshing a recommendation engine, the method comprising: providing a social media platform configured to supply a content feed to a user computing device of a user; generating user content interaction information by detecting user interactions with the content feed; providing a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information; receiving a refresh request for refreshing the recommendation engine; as well as In response to receiving the refresh request, the recommendation engine is refreshed at least in part by masking or resetting the user-content interaction information.

13. The computerized method of claim 12, further comprising: inputting the masked or reset user content interaction information into the recommendation engine; generating, via the recommendation engine, a refreshed content item based on the masked or reset user content interaction information; as well as The refreshed content item is transmitted to the user computing device for display in the content feed.

14. The computerized method of claim 12, further comprising: supplying a graphical user interface GUI configured to display the content feed to a user computing device, the GUI comprising a refresh selector; as well as The refresh request is received from the user computing device when a user selects the refresh selector.

15. The computerized method of claim 12, further comprising: The content item is selected for display, via the recommendation engine, based on the generated user-content interaction information and user information, wherein the user information includes at least a user identification for the user.

16. The computerized method of claim 15, further comprising: In response to receiving the refresh request, the recommendation engine is refreshed at least in part by additionally masking or resetting the user information including the user identification.

17. The computerized method of claim 16, wherein the refreshing of the recommendation engine comprises a first temporary refresh that masks or resets the user content interaction information and the user information including the user identification up to a first predetermined threshold number of views.

18. The computerized method of claim 17, wherein the refreshing of the recommendation engine comprises a second interim refresh that unmasks the user content interaction information and the user information except for the user identification, and masks the user identification until a second predetermined threshold number of views.

19. The computerized method of claim 12, further comprising: generating embedded information based on the user content interaction information; as well as The recommendation engine is refreshed by masking or resetting the user content interaction information or by masking or resetting the embedded information representing the user content interaction information.

20. A computer readable medium comprising instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform the following steps: providing a social media platform configured to supply a content feed to a user computing device of a user; generating user content interaction information by detecting user interactions with the content feed; providing a recommendation engine that selects content items for display in the content feed based on the generated user content interaction information, user information including at least a user identification, and device information of the user; receiving a refresh request for refreshing the recommendation engine; In response to receiving the refresh request, refreshing the recommendation engine at least in part by masking or resetting the user content interaction information and the user information; inputting the masked or reset user content interaction information, the masked or reset user information, and the device information of the user into the recommendation engine; generating, via the recommendation engine, refreshed recommended content items based on the masked or reset user content interaction information and the masked or reset user information; as well as The refreshed content item is transmitted to the user computing device for display in the content feed.

Images