Abstract: System and method for generating a ranked list are disclosed. A plurality of prior interactions for a first customer are received by a computing device. Each of the prior interactions includes a product interaction and time. A ranked list of item recommendations is generated based on the plurality of prior interactions. The ranked list of item recommendations is generated by a trained prediction model trained using temporal information embedded into a finite-dimensional vector space. The ranked list of item recommendations is output by the computing device.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform acts of: gathering historical data, which can comprise interactions of a user with a first graphical user interface at a first time; storing the historical data which can comprise the interactions of the user with the first graphical user interface as at least one historical feature vector; gathering in-session data, which can comprise interactions of the user with a second graphical user interface at a second time later than the first time; storing the in-session data which can comprise the interactions of the user with the second graphical user interface as at least one in-session feature vector; determining an intent of the user using the at least one historical feature vector and the at least one in-session feature vector; and transmitting instructions to display a third graphical user interf

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: modeling a webpage as a random field, wherein the random field comprises an undirected graph comprising nodes and edges; identifying a cyclic dependency in the undirected graph, wherein the cyclic dependency involves at least three of the nodes; breaking one or more of the edges of the undirected graph that connects the at least three of the nodes in the cyclic dependency; determining a probability of the webpage having exceeded a predetermined threshold based on compatibility functions of the edges, as updated; and sending instructions to display the webpage based at least in part on the probability of the webpage having exceeded the predetermined threshold. Other embodiments are described.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform acts of: gathering historical data, which can comprise interactions of a user with a first graphical user interface at a first time; storing the historical data which can comprise the interactions of the user with the first graphical user interface as at least one historical feature vector; gathering in-session data, which can comprise interactions of the user with a second graphical user interface at a second time later than the first time; storing the in-session data which can comprise the interactions of the user with the second graphical user interface as at least one in-session feature vector; determining an intent of the user using the at least one historical feature vector and the at least one in-session feature vector; and transmitting instructions to display a third graphical user interf

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: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving a plurality of user reviews of a product, performing topic modeling of the plurality of user reviews of the product to find a plurality of snippets within the plurality of user reviews each relating to at least one user attribute category of a plurality of user attribute categories, and facilitating a display on a device of a first snippet of the plurality of snippets proximate the product.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing first transaction data stored in a transaction database, the first transaction data describing first transactions for first items from first users; determining, using the first transaction data, first micro-intents associated with the first transaction data; grouping the first micro-intents into clusters; labeling each cluster of the first micro-intents with a respective label; receiving second transaction data of a user, the second transaction data describing second transactions for second items for the user; determining, using the second transaction data, second micro-intents present in the second transactions; receiving current transaction data from a user interface of an electronic device of the user; determining, using the current transaction data, that the user is expressing a curre

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 method including tracking usage data for users using a first channel and a second channel. The method also can include performing a classification of first users of the users into a first group and second users of the users into a second group. The classification can be based on the usage data. The method additionally can include, for each impression of a content element of content elements being displayed on a website to a user of the users, tracking impression response data including (a) whether the user is grouped into the first group or the second group, and (b) response data including: (i) a first response by the user to the content element in the first channel, and (ii) a second response by the user to the content element in the second channel. The method further can include receiving a request from a first user of the users to display a webpage of the website. The method additionally can include generating the webpage to include a selected content element from among the content elements.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: utilizing historical transaction information to derive metric information associated with prior transactions; generating a listing of user-item pairs, each of the user-item pairs identifying a user and an item; executing a machine learning model that is configured to generate a transmission list for sending push notifications; generating a transmission list by selecting user-item pairs based on the conversion probability values and the confidence indicators that are assigned to the user-item pairs; customizing content for the push notifications to include information for items identified by the user-item pairs included in the transmission list; and transmitting the push notifications to the users identified by the user-item pairs included in the transmission list.

Abstract: In some embodiments, a method can comprise receiving a search query from a search by a user during a browse session, receiving one or more items from an item database in response to the search query, and receiving one or more previous search queries from a search database, the one or more previous search queries related to the search query. In many embodiments, the method can further comprise determining a purchase probability associated with a first item of the one or more items based at least in part on a first item score for the first item, ranking the one or more items based at least in part on the purchase probability associated with the first item of the one or more items, and facilitating display of the ranking of the one or more items. Other embodiments of related methods and systems are also provided.

Abstract: In some embodiments, a method can comprise receiving a search query from a search by a user during a browse session, receiving one or more items from an item database in response to the search query, and receiving one or more previous search queries from a search database, the one or more previous search queries related to the search query. In many embodiments, the method can further comprise determining a purchase probability associated with a first item of the one or more items based at least in part on a first item score for the first item, ranking the one or more items based at least in part on the purchase probability associated with the first item of the one or more items, and facilitating display of the ranking of the one or more items. Other embodiments of related methods and systems are also provided.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving a plurality of user reviews of a product, performing topic modeling of the plurality of user reviews of the product to find a plurality of snippets within the plurality of user reviews each relating to at least one user attribute category of a plurality of user attribute categories, and facilitating a display on a device of a first snippet of the plurality of snippets proximate the product.

Abstract: According to an example, a method for supplier quantity selection may include receiving a plurality of supplier offers for one or more items to be procured by a manufacturer, and determining whether the supplier offers include price uncertainty and a most favored customer (MFC) clause. Based on the determination that the supplier offers include price uncertainty and the MFC clause, the method may include evaluating the supplier offers by analyzing the price uncertainty and the MFC clause using a stochastic multi-stage model. The method may further include determining, by a processor, an allocation of all or part of each of the supplier offers to minimize purchase price of the one or more items based on the evaluation of the supplier offers.

Abstract: An application for personal project management stored on a computer-readable medium, the application includes a workflow engine; a constraint programming solver and a user interface configured to receive input from a user input device to create a workflow model of a personal project. The constraint programming solver is configured to modify the workflow model during both design and run-time by adding suggestions or advertisements. The workflow engine is configured to present the workflow model through the user interface.