Method and system for suggesting messages and accounts from a real-time messaging platform

A real-time messaging platform and method are disclosed which suggests messages and accounts from the real-time messaging platform.

BACKGROUND OF THE INVENTION

The present invention relates to real-time messaging platforms and, more particularly, for suggesting messages and accounts from a real-time messaging platform.

There are a wide range of known automatic techniques for analyzing content in order to generate suggestions for a user or a set of users of a service. For example, with regard to textual content, there are known techniques from the areas of textual categorization, textual clustering, and entity extraction that can be used to classify the different textual content (there are similar classification techniques for other types of content, such as audio and video). The classification result can then be used to determine what type of other content to associate with the classified content. Such mechanisms have been used to insert related content, such as textual snippets, into websites, as implemented for example in the Google Adsense program. Other techniques, such as collaborative filtering and nearest neighbor approaches have also been devised to generate recommendations without relying on any explicit content analysis.

SUMMARY OF THE INVENTION

A real-time messaging platform and method are disclosed which suggests messages and accounts from the real-time messaging platform. In accordance with an embodiment of the invention, an initial set of user accounts are associated with arbitrary content, such as a website, based for example on prior access statistics regarding the content. The accounts in the real-time messaging platform have associated graph data. A set of suggested accounts in the real-time messaging platform are selected based on the graph data of the initial accounts. The suggested accounts can be selected and ranked based on an advantageous scoring metric, for example, based on counts of a number of users with a pre-specified graph relationship with the suggested account. The suggested accounts are associated with the content so that messages associated with the suggested accounts can be selected for the content. Also, a user's account and content access statistics can be used to generate accounts to suggest to the user using the techniques disclosed herein.

The disclosed technique is thereby capable of generating suggested accounts and associated messages with content without resorting to direct analysis or classification of the content. Alternatively, the content of the website can be analyzed and used as backoff mechanism or to supplement and guide the selection of messages from the real-time messaging platform.

Details of one or more embodiments are set forth in the accompanying drawings and description below.

DETAILED DESCRIPTION

FIG. 1is a conceptual representation of the relationship between data in the real-time messaging system and the content, when processing is performed in accordance with an embodiment of the invention. It is assumed that the real-time messaging platform, as further described below, facilitates messaging from a set of user accounts, each account having associated user graph data.

FIG. 1depicts a set of content110, a set of initial user accounts120, and a set of suggested accounts130. The set of content110inFIG. 1can be advantageously virtually any form of content made available to a set of users, e.g., including textual content, audio, visual or some other form of multimedia content. For example, the content can be web pages of a website made available to users through a browser or some other form of presentation client. The content can be some form of audio/visual content broadcast to users through some distribution infrastructure, including television. It is assumed that user statistics are available for the particular content to be processed, as further described herein.

InFIG. 1, a set of initial user accounts120is derived from the set of content110. The initial user accounts120are a subset of accounts of users of a real-time messaging platform. The initial user accounts120are derived based on prior user accesses or interaction with the content110, as reflected in content access statistics. The initial user accounts120are then used to derive a set of suggested accounts130in the real-time messaging platform. The set of suggested accounts130is identified based on the user graph data associated with the initial user accounts120, as further described below. Then, the suggested accounts130can be associated with the content so that messages associated with the suggested accounts can be selected for the content. For example, where the content is a website, suggested accounts and associated messages can be inserted into the website, e.g., using a gadget implemented using a browser script. Where the content is a television broadcast, associated messages can be inserted into an overlay or a separate message interface of the broadcast.

FIGS. 2 and 3are flowcharts of processing performed in accordance with an embodiment of an aspect of the invention.

At step201inFIG. 2, content access statistics are retrieved which reflect prior access or interaction with the content by a set of users. This can reflect, for example, views by users of the content previously. Alternatively, or in addition, it can reflect prior clicks or hover events or other forms of engagement with the content. The content access statistics can be obtained using a number of known techniques, including simple log analysis of user interactions with the relevant content.

At step202, a determination is made of whether the statistics meet some minimum threshold, e.g., whether the count of views by users is less than some minimum threshold number of viewings. If it does not meet the threshold, then, at step209, various backoff techniques can be performed. If it does meet the threshold, then processing continues inFIG. 2. The concept of backoff is further described below.

At step203, the content access statistics are used to identify and retrieve a set of initial user accounts, the initial user accounts associated with the content interactions/accesses. The initial user accounts are a subset of all user accounts in a real-time messaging platform, an embodiment of which is further described below. Each user account is assumed to have associated user graph data reflecting a graph relationship with other user accounts. At step204, this user graph data from each initial user account is used to retrieve a set of candidate user accounts. For example, the candidate user accounts can be selected from the set of user accounts which are being “followed” by the initial user accounts. The candidate user accounts can comprise all of the user accounts with a certain type of graph relationship with the initial user accounts or can be a subset of all such user accounts with such relationship with the initial user accounts.

At step205to207, each candidate user account in the set of candidate user accounts retrieved in step204are scored at step206. The optimal scoring candidate accounts are then selected and associated with the content at step208. The score can reflect a sense of the popularity of the candidate user account as a suggested user account for the content.

A variety of scoring metrics can be used. For example, the candidate user accounts can be scored based in part on a number of accounts in the real-time messaging platform with a pre-specified graph relationship in the user graph data. This reflects the intuition that if significant numbers of user accounts who access or interact with the content also have the particular graph relationship to a candidate account in the real-time messaging platform, then this candidate account is likely to be of interest to other users who access the same or related content. Where the user graph data includes a graph relationship based on the concept of “following” another account of interest, for example, a useful scoring metric can be constructed based on a count of “followers” of an account. For example, the scoring metric can be based on the following equation:

score=FvF*log⁡(T)F(equation⁢⁢1)
where is a count of followers of the candidate account who also accessed or interacted with the content as part of the content access statistics, F is a count of total followers of the candidate account, and T is a total count of accounts in the real-time messaging platform. This equation is inspired by the concept of term frequency-inverse document frequency weighting schemes in the context of document keyword selection/weighting techniques.

The scoring metric can be based on different variations of the relationship between followers who accessed the content and the total followers of the candidate account, such as:

score=FvF(equation⁢⁢2)
Alternatively, the scoring metric can use a variant where, for example, the square root of the followers is utilized. Alternatively, and without limitation, the scoring metric can take into account statistics from the content access statistics. For example, the scoring metric can be:

score=FvP(equation⁢⁢3)
where P is a statistic derived from the content access statistics, such as a number of page views where the content is a web page. Or the scoring metric can be a combination of the above that takes into account content access statistics, such as:

Alternatively, and without limitation, the scoring metric can take into account a reputational metric derived from the user graph data in the real-time messaging platform. For example, if the count of followers who have accessed the content exceeds some threshold, the score can be assigned the reputational score for the candidate account—or some variant score based on the reputational score for the candidate account. Any advantageous reputational scoring mechanism for the particular user graph data can be utilized. Additionally and alternatively, the scoring metric can take into account other possible signals, such as message location, inferred characteristics about the account, and a variety of other signals about the above-mentioned accounts and messages.

The associations between the suggested accounts and the content can be generated using the processing performed inFIG. 2and stored and utilized to associate messages with the content in an automated batch mode fashion.FIG. 3is a flowchart of processing performed using the stored associations to provide messages from the messaging platform in real-time.

At step301, the suggested accounts associated with the content are retrieved. The content can be identified in any arbitrary manner, including by some form of address, such as by URL or by domain. At step302through304, each suggested account in the list of associated suggested accounts is processed. At step303, messages associated with each suggested account are retrieved. At step305, the messages associated with each suggested account are combined. The messages can be combined in any arbitrary organizational manner, for example, by timestamp or by some other organizational metric. Then, at step306, the combined messages can be associated with the content, for example, by inserting the messages with the content in some advantageous fashion. For example, where the content is a webpage of a website, the messages can be communicated to a gadget or some other embedded script within the webpage for insertion of the messages into the content of the webpage, as further described below.

A variety of mechanisms can be employed to supplement the disclosed techniques. As mentioned above, where the content access statistics for a particular content are insufficient to generate useful suggested accounts, one of a number of different backoff mechanisms can be utilized. For example, statistics for related content can be collected and used with or in lieu of the existing content access statistics. Where the content to be processed is content on a website, e.g., related content from different URLs or content from the rest of the domain can be aggregated or used to generate suggested accounts. Alternatively, and without limitation, content-based mechanisms can be used as an alternative backoff mechanism, or can be used to supplement the above-disclosed techniques. For example, content-based mechanisms which process and analyze the relevant content can be used to supplement the above-disclosed scoring techniques.

FIG. 4is a diagram of an embodiment of a real-time messaging platform410. The real-time messaging platform410includes a routing module421, a fanout module422, a delivery module423, various storage modules431,432,433, and a frontend module440.

A user of the platform composes a message401to be sent from an entry point. The entry point can be based on the operation of any computing device, for example, a mobile phone, a personal computer (laptop, desktop, or server), or a specialized appliance having communication capability. The entry point can utilize any of a number of advantagous interfaces, including a web-based client, a Short Messaging Service (SMS) interface, an instant messaging interface, an email-based interface, an API function-based interface, etc. The message401can be transmitted through a communication network to the real-time messaging platform410.

The routing module421in the real-time messaging platform410receives the message401and proceeds to store the message401in a message storage module431. The message401is assigned an identifier. The sender of the message is passed to a fanout module422. The fanout module422is responsible for retrieving user graph data from the user graph storage module431and using the user graph data to determine which accounts in the real-time messaging platform410should receive the message401. The user graph data, for example, can reflect which accounts in the real-time messaging platform are “following” a particular account and are, therefore, subscribed to receive status messages from the particular account. The user graph data can reflect more sophisticated graph relationships between the accounts. The delivery module423takes the list of accounts from the fanout module422and the message identifier generated by the routing module421and proceeds to insert the message identifier into message list data associated with each identified account and stored in the message list storage module433. The message lists stored in the timeline storage module433can be a “timeline” of messages associated with the account or can reflect any arbitrary organization of the messages that is advantageous for the user of the account on the real-time messaging platform410.

The frontend module440uses the storage modules431,432,433to construct message lists for serving to a user of the account on the real-time messaging platform410. As with the entry point, a user can use any end point to receive the messages405. The end point can also be any computing device providing any of a number of advantageous interfaces. For example, where the user uses a web-based client to access their messages405, a web interface module441in the front end440can be used to construct the message lists and serve the lists to the user. Where the user uses a client that accesses the real-time messaging platform410through an API, an API interface module442can be utilized to construct the message lists and serve the lists to the client for presentation to the user. Similarly, different forms of message delivery can be handled by different modules in the front end440. The user can specify particular receipt preferences which are implemented by the modules in the front end440.

Module460can be integrated with the real-time messaging platform410. Module460implements the processing set forth inFIG. 2. Module460receives content access statistics450and user graph data from the user graph storage module432and proceeds to generate suggested accounts associated with particular content, which are stored in the suggested user storage module470.

Module470can also be integrated with the real-time messaging platform410. Module470implements the processing set forth inFIG. 3. Module470uses the associations between content and suggested accounts stored in the suggested users storage module470to select messages for the content through integration with the front end440. For example, where the content is an electronic document such as a webpage of a website with a content module, such as a gadget integrated with the content in the website, the content module can call the API interface module442through an advantageous API. The front end440can then serve messages from the real-time messaging platform410for presentation through the gadget with the content on the website.

Each module illustrated inFIG. 4can be implemented using software executing on generic processing hardware, comprising a processor and memory. Each module can represent a separate process or thread or other execution context running on the same or different machines. Similarly, and without limitation, the modules can be represented through equivalent electrical circuits, microcode, or other implementations of the described functionality.

FIG. 5illustrates an exemplary content module510embedded in an electronic document500such as a webpage of a website. The electronic document500can be embodied as any form of Internet content, e.g., as pages of HTML markup or XML markup or as executable Java or Javascript, etc. The content module510can be a separate executable program (such as a plugin) or can be an integrated script, such as Javascript integrated with the rest of the electronic document500. Content module510communicates with the real-time messaging platform410, for example by communicating with the API interface module442. The content module510provides an identifier, for example, the URL associated with the electronic document500. The real-time messaging platform410, using the above-disclosed technique, provides suggested accounts and/or suggested messages associated with the content of the electronic document500through the API. As depicted inFIG. 5, content module510can display the suggested accounts511,512and the suggested messages515,516,517. Notably, the content module510can also be used to include promoted content, such as promoted accounts and promoted messages, along with the suggested accounts and suggested messages.

Multiple embodiments have been described, and it will be understood that various modifications can be made without departing from the spirit and scope of the invention. For example, different forms of content can be processed, as well as different real-time messaging platform architectures can be utilized and different scoring metrics for candidate accounts. Accordingly, other embodiments are within the scope of the following claims.