Systems and methods for predicting tasks that users perform on websites

1 . A computing system for predicting tasks that a user performs on a website, the system comprising a server computing device having a memory for storing computer-executable instructions and a processor that executes the computer-executable instructions to:

train a machine learning model, at a first stage, based on page session data corresponding to each of one or more users who have previously accessed the website, wherein the page session data includes one or more page flows, each page flow being a sequence of webpages on the website that have been accessed in a single user session, and wherein, during the first stage, each webpage in a page flow is interpreted as a lexical token and the machine learning model is trained to generate a page embedding for each lexical token based upon co-occurrence within page flows;

train the machine learning model, at a second stage, based on the page session data that is associated with the user, user attribute data, and user task data, and wherein the second stage comprises fine-tuning the machine learning model to predict a task associated with a training page flow using only a partial sequence of webpages from the training page flow;

determine, by the machine learning model, during an ongoing user session and prior to completion of a task, one or more predicted tasks after a predetermined number of pages on the website have been accessed by the user in the user session, wherein the one or more predicted tasks are determined based on at least one of the predetermined number of pages and the user attribute data; and

generate one or more recommended content items based on the one or more predicted tasks, wherein the recommended content items are displayed to the user on a page of the website that is currently being accessed by the user and wherein the recommended content items are generated independently of an explicit user approval to create a task record.

2 . The computing system of claim 1 , wherein the page of the website that is currently being accessed by the user is generated by modifying the selected page, wherein the modification of the page includes rearranging one or more existing content items on the page to accommodate the one or more recommended content items.

3 . The computing system of claim 1 , wherein the page of the website that is currently being accessed by the user includes a chatbot, and wherein the chatbot includes an input section to receive queries from the user, an output section to display a response to the queries, and a recommended content section that includes the one or more recommended content items.

4 . The computing system of claim 3 , wherein the recommended content items in the recommended content section are each associated with a query that was generated or extracted based on a corresponding predicted task of the one or more predicted tasks.

5 . The computing system of claim 1 , wherein interpreting each webpage as a lexical token comprises mapping each webpage to a discrete page identifier representing the webpage within the machine learning model.

6 . The computing system of claim 5 , wherein the discrete page identifier is derived from at least one of a URL, a canonicalized URL, a page hash, or an internal page reference.

7 . The computing system of claim 5 , wherein the page embedding is generated based on observed transitions between discrete page identifiers within page flows.

8 . The computing system of claim 1 , wherein the partial sequence of webpages excludes at least one webpage associated with completion of the predicted task.

9 . The computing system of claim 8 , wherein the predicted task is determined prior to user interaction with a webpage that initiates execution of the task.

10 . A non-transitory computer-readable medium including computer-executable instructions that, when executed by a computing device, causes the computing device to:

train a machine learning model, at a first stage, based on page session data corresponding to each of one or more users who have previously accessed the website, wherein the page session data includes one or more page flows, each page flow being a sequence of webpages on the website that have been accessed in a single user session, and wherein, during the first stage, each webpage in a page flow is interpreted as a lexical token and the machine learning model is trained to generate a page embedding for each lexical token based upon co-occurrence within page flows;

train the machine learning model, at a second stage, based on the page session data that is associated with the user, user attribute data, and user task data, and wherein the second stage comprises fine-tuning the machine learning model to predict a task associated with a training page flow using only a partial sequence of webpages from the training page flow;

determine, by the machine learning model, during an ongoing user session and prior to completion of a task, one or more predicted tasks after a predetermined number of pages on the website have been accessed by the user in the user session, wherein the one or more predicted tasks are determined based on at least one of the predetermined number of pages and the user attribute data; and

generate one or more recommended content items based on the one or more predicted tasks, wherein the recommended content items are displayed to the user on a page of the website that is currently being accessed by the user and wherein the recommended content items are generated independently of an explicit user approval to create a task record.

11 . The non-transitory computer-readable medium of claim 10 , wherein the computer executable instructions cause the computing device to perform further operations to:

extract, in real time, a plurality of accessed webpages that have been accessed by the user within a predetermined time period.

12 . The non-transitory computer-readable medium of claim 11 , wherein the machine learning model determines at least one predicted task after an end of the predetermined time period, wherein the at least one predicted task is determined by the machine learning model based on the plurality of accessed webpages.

13 . The non-transitory computer-readable medium of claim 11 , wherein the computer executable instructions cause the computing device to perform further operations to:

determine one or more accessed webpages of the plurality of accessed webpages, the one or more accessed webpages being a subset of the plurality of accessed webpages,

wherein the machine learning model determines at least one predicted task after an end of the predetermined time period, and wherein the at least one predicted task is determined by the machine learning model based on the one or more accessed webpages.

14 . The non-transitory computer-readable medium of claim 10 , wherein the computer executable instructions cause the computing device to perform further operations to:

retrieve trend data corresponding to one or more trends associated with completing tasks by other users at predetermined time intervals, wherein the trend data corresponds to trends identified over a predetermined period of time; and

train the machine learning model based on the trend data, wherein the predicted task is determined based in part on the trend data.

15 . The non-transitory computer-readable medium of claim 10 , wherein each page on the website includes a page identifier that is mapped to one or more tasks, and wherein the machine learning model determines the predicted task based on a sequence of page identifiers corresponding to the one or more predetermined number of pages.

16 . The non-transitory computer-readable medium of claim 10 , wherein the user task data includes one or more tasks that have been previously completed by the user on the website.

17 . A computerized method for predicting tasks that a user performs on a website, the method comprising:

training a machine learning model, at a first stage, based on page session data corresponding to each of one or more users who have previously accessed the website, wherein the page session data includes one or more page flows, each page flow being a sequence of webpages on the website that have been accessed in a single user session, and wherein, during the first stage, each webpage in a page flow is interpreted as a lexical token and the machine learning model is trained to generate a page embedding for each lexical token based upon co-occurrence within page flows;

training the machine learning model, at a second stage, based on the page session data that is associated with the user, user attribute data, and user task data, and wherein the second stage comprises fine-tuning the machine learning model to predict a task associated with a training page flow using only a partial sequence of webpages from the training page flow;

determining, by the machine learning model, during an ongoing user session and prior to completion of a task, one or more predicted tasks after a predetermined number of pages on the website have been accessed by the user in the user session, wherein the one or more predicted tasks are determined based on at least one of the predetermined number of pages and the user attribute data; and

generating one or more recommended content items based on the one or more predicted tasks, wherein the recommended content items are displayed to the user on a page of the website that is currently being accessed by the user and wherein the recommended content items are generated independently of an explicit user approval to create a task record.

18 . The method of claim 17 , wherein the page session data further includes, for each page flow, a time corresponding to when each webpage in the page flow was accessed, and a page identifier associated with each webpage in the page flow.