Abstract: In many embodiments, a system comprising one or more processor and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of questions associated with a campaign by extracting text from one or more advertisements; generating vector embeddings of one or more online activities of a user; receiving a search query from a graphical user interface of a computing device of the user; determining a second question from the set of questions to present to the user based on respective relevance probabilities; determining a confidence score associated with the second question; and when the confidence score associated with the second question exceeds a second predetermined threshold, presenting the second question to the user via the graphical user interface. Other embodiments of related methods and systems are also provided.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a plurality of snippets of a plurality of user reviews for a product, each respective snippet of the plurality of snippets relating to at least one respective user attribute category of a plurality of user attribute categories; creating a score for each respective snippet of the plurality of snippets based on: a probability of association between at least one user attribute category and one or more seed words, the one or more seed words describing one or more qualities of the product; and facilitating displaying, on a user device of a user, a first snippet of the plurality of snippets, the first snippet of the first plurality of snippets having a higher score of the scores for the plurality of snippets than another score of the scores for the plurality of snippets. Other embodiments are disclosed herein.

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform recording one or more actions of a user during an online browsing session; predicting, in real-time, a first user attribute for the user from one or more user attributes during the online browsing session based on the one or more actions of the user during the online browsing session; correlating the first user attribute, as predicted, for the user, wherein a first user preference of one or more user preferences is associated with the first user attribute, as predicted; and mapping each respective user preference of the one or more user preferences to a vector space, wherein: two or more user preferences of the one or more user preferences determined to be more similar to one another are closer together on the vector space than two or more user preferences of the one or more user preferences determined to be less similar

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: A method including training a logistic regression model to create a trained model to provide probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods. Input predictor variables of the logistic regression model include (i) user feature data including personal user features and online activity history for users in the multiple different time periods and (ii) email feature data including sent times and item category data for multiple different emails in the one or more email campaigns. Output dependent variables of the logistic regression model include responses by the users to the one or more email campaigns. The method also includes triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform tracking, using the one or more processors, usage history of a user; creating training data from the usage history of the user; training a predictive algorithm on the training data; for each respective promotion in a plurality of promotions: assigning a respective weight to the respective promotion of the plurality of promotions by using the predictive algorithm, as trained; after a predetermined time period has elapsed, updating the usage history of the user; re-training the predictive algorithm, as trained, on the usage history of the user, as updated; for each respective promotion in the plurality of promotions: assigning a new respective weight to the respective promotion of the plurality of promotions using the predictive algorithm, as trained and re-trained; and coordinating displ

Abstract: A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.

Abstract: A method including tracking first data including responses by users to email campaigns. The method also can include generating a user feature set for the users, the user feature set including personal features of each of the users and online activity history for each of the users across a first set of time periods. The method additionally can include generating an email feature set for the emails in the email campaigns. The method further can include training a logistic regression model to create a trained model using the first data, the user feature set, and the email feature set to provide a probability of a user of the users clicking on an email of the emails within each time period of a second set of time periods. The method additionally can include triggering sending a first email of the email campaigns to a first user of the users at a selected time period of the second set of time periods based at least in part on the trained model. Other embodiments are disclosed.

Abstract: A system and method for providing a customized user experience is presented. A system can include one or more processing modules and one more non-transitory storage modules. The usage history of a user can be tracked at an eCommerce retailer. Static information about the user can also be tracked. Thereafter, using the usage history, the static information, and the available promotions of the retailer, a probability of conversion can be calculated for each promotion. Thereafter, the promotion being displayed to the user can be based on the probability of conversion. Other embodiments are also disclosed herein.

Abstract: A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: inputting one or more pairs of digital images into a neural network; determining, using the neural network, a respective contrastive loss for each respective pair of digital images of the one or more pairs of digital images; receiving one or more new digital images; and determining, using the neural network, the one or more new digital images and the respective contrastive loss, at least one image to which the one or more new digital images is similar. Other embodiments are disclosed herein.

Abstract: A method can include modeling a webpage as a random field. The random field can include an undirected graph including two or more nodes and one or more edges. A goodness function can be associated with one or more webpage elements. Each edge of the one or more edges can include a compatibility function based at least in part on the one or more goodness functions of two different nodes of the two or more nodes. The method also can include determining a probability of the webpage having exceeded a predetermined threshold based at least in part on one or more of the compatibility functions of the one or more edges. Other embodiments are disclosed.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: coordinating displaying a personalized list of recommended items on a graphical user interface of a user device of a user; determining a personal replenishment cycle for an item of a set of items previously purchased by the user; increasing available storage space in the one or more non-transitory computer-readable media of the system by removing a first item from the set of items to identify that the user has stopped purchasing the first item; and increasing a network bandwidth of the system by reducing a number of database requests for the set of items previously purchased by the user. Other embodiments are disclosed.

Abstract: Many embodiments can include a system. In some embodiments, the system can comprise one or more processors and one or more non-transitory storage devices storing computing instructions are disclosed.

Abstract: In many embodiments, a system comprising one or more processor and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of questions associated with a campaign by extracting text from one or more advertisements; generating vector embeddings of one or more online activities of a user; receiving a search query from a graphical user interface of a computing device of the user; determining a second question from the set of questions to present to the user based on respective relevance probabilities; determining a confidence score associated with the second question; and when the confidence score associated with the second question exceeds a second predetermined threshold, presenting the second question to the user via the graphical user interface. Other embodiments of related methods and systems are also provided.

Abstract: A method including coordinating displaying a personalized list of recommended items on a graphical user interface of a user electronic device of a user. Coordinating displaying can occur after applying a first set of rules to historical sales data available to a retailer to detect a personal replenishment cycle for an item of a set of items purchased by the user. The personal replenishment cycle for the item purchased by the user can comprise an estimated time period of how often the user purchases the item. The historical sales data can comprise a record of the user purchasing the item on one or more dates. Coordinating displaying further can occur after applying a second set of rules to identify if the user has stopped purchasing the item. Coordinating displaying also can occur after applying a third set of rules to the personal replenishment cycle for the item to identify the personalized list of recommended items for the user to consider replenishing. Other embodiments are disclosed.

Abstract: In many embodiments, the method can comprise receiving a search query from a search by a user and determining a question to present to the user. In many embodiments, determining the question to present to the user can comprise evaluating a user profile associated with the user, evaluating the search query, evaluating one or more user actions during a current browse session of the user, and selecting the question from a set of questions. In some embodiments, the method can further comprise presenting the question to the user when a confidence score associated with the question reaches or exceeds a predetermined threshold. Other embodiments of related methods and systems are also provided.

Abstract: A system including one or more processors and one or more non-transitory media storing computer instructions configured to run on the one or more processors and perform: identifying a first sub-population of case individuals from a gross population of the case individuals; presenting first test content to a first test sub-population of the case individuals, the first test content is selected according to a first statistical model; measuring a first test sub-population average feedback metric based on first test content feedback provided from the first test sub-population of the case individuals; determining that the first test sub-population average feedback metric exceeds a first control population average feedback metric of a first control population of the case individuals; and determining that a probability value for a difference between the first test sub-population average feedback metric and the first control population average feedback metric is less than a predetermined significance level value.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform recording one or more actions of a user during an online browsing session; predicting, in real-time, a first user attribute for the user from one or more user attributes during the online browsing session based on the one or more actions of the user during the online browsing session; correlating the first user attribute, as predicted, for the user, wherein a first user preference of one or more user preferences is associated with the first user attribute, as predicted; and mapping each respective user preference of the one or more user preferences to a vector space, wherein: two or more user preferences of the one or more user preferences determined to be more similar to one another are closer together on the vector space than two or more user preferences of the one or more user preferences determined to be less similar