Abstract: Systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (GCS) model are disclosed. A request for an interface is received and a set of interface items is generated for inclusion in the interface. The set of interface items is selected, at least in part, by a GCS model including a semantic similarity component and a viewed-also-viewed component. The set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. The interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.

Abstract: This application is directed to systems and methods for organizing information. In some embodiments, a disclosed method includes receiving a plurality of requests associated with a plurality of items corresponding to a plurality of item types, where each item type includes one or more items; selecting, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests; for each item type of the subset of target item types, selecting a set of boosting items from a set of target items of the respective target item type based on an item score of each of the set of target items; and generating an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types.

Abstract: Systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (GCS) model are disclosed. A request for an interface is received and a set of interface items is generated for inclusion in the interface. The set of interface items is selected, at least in part, by a GCS model including a semantic similarity component and a viewed-also-viewed component. The set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. The interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.

Abstract: Systems and methods for generating dynamic interfaces using explore-exploit models is disclosed. A first interface including a first set of content modules selected from a set of candidate content modules is generated and feedback data representative of one or more interactions with the first interface is received. The feedback data includes positive feedback and negative feedback. A set of weights for each content module is generated by a contextual explore-exploit model based on the feedback data. A request for a second interface including a context is received and a set of ranked content modules is selected for the context based on the weights of each of the content modules in the set of candidate content modules. A second interface including a second set of content modules is generated. The second set of content modules includes a predetermined number of top ranked content modules in the set of ranked content modules.

Abstract: Systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (GCS) model are disclosed. A request for an interface is received and a set of interface items is generated for inclusion in the interface. The set of interface items is selected, at least in part, by a GCS model including a semantic similarity component and a viewed-also-viewed component. The set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. The interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.

Abstract: Systems and methods for content selection and presentation are disclosed. Training data including indicating one or more interactions with one or more content elements and associated with one of a plurality of individual contexts is received. A selection model is trained by applying a reinforcement learning mechanism and an individual explore-exploit mechanism. A context for a user is selected by applying the selection model, which is configured to determine an expected future reward value of at least one of the plurality of individual contexts, determine an expected future reward value of a global context based on a past click-through rate, a reward value, and a future click-through rate, and, select the global context or one of the one or more individual contexts based on a comparison of the expected future reward values.

Abstract: Systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (GCS) model are disclosed. A request for an interface is received and a set of interface items is generated for inclusion in the interface. The set of interface items is selected, at least in part, by a GCS model including a semantic similarity component and a viewed-also-viewed component. The set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. The interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.

Abstract: Systems and methods for improving diversification in online item recommendations are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request seeking recommendations to be displayed on a webpage of a website to a user; obtaining user session data identifying website activities of users on the website; determining weights of a set of content elements associated with the website based on posterior distributions utilizing the user session data; modifying the weights by at least one predetermined parameter to generate updated weights based on contextual feature data of the set of content elements; generating, from the set of content elements, a ranked list of content elements as recommended content based on the updated weights of the set of content elements; and transmitting, to the computing device, the recommended content in response to the recommendation request.

Abstract: Systems and methods for generating dynamic interfaces using explore-exploit models is disclosed. A first interface including a first set of content modules selected from a set of candidate content modules is generated and feedback data representative of one or more interactions with the first interface is received. The feedback data includes positive feedback and negative feedback. A set of weights for each content module is generated by a contextual explore-exploit model based on the feedback data. A request for a second interface including a context is received and a set of ranked content modules is selected for the context based on the weights of each of the content modules in the set of candidate content modules. A second interface including a second set of content modules is generated. The second set of content modules includes a predetermined number of top ranked content modules in the set of ranked content modules.

Abstract: Systems and methods for content selection and presentation are disclosed. Training data including indicating one or more interactions with one or more content elements and associated with one of a plurality of individual contexts is received. A selection model is trained by applying a reinforcement learning mechanism and an individual explore-exploit mechanism. A context for a user is selected by applying the selection model, which is configured to determine an expected future reward value of at least one of the plurality of individual contexts, determine an expected future reward value of a global context based on a past click-through rate, a reward value, and a future click-through rate, and, select the global context or one of the one or more individual contexts based on a comparison of the expected future reward values.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform operations including: automatically customizing, based on a first state of a user, first content for a graphical user interface on an electronic device of the user; monitoring second activities of the user over a time period; identifying a second probability that the user has transitioned from the first state into a second state during the time period; determining when the second probability is above a second probability predefined threshold; and after determining the second probability to be above the second probability predefined threshold, automatically customizing, based on the second state of the user, second content for the graphical user interface on the electronic device of the user. Other embodiments are disclosed herein.

Abstract: A system and method for content selection and presentation is disclosed. A plurality of content elements configured for presentation in at least one content container is received and one of the plurality of content elements is selected for presentation in the at least one content container. The one of the plurality of content elements is selected by a trained selection model configured to select one of an individual context or a global context. An interface including the selected one of the plurality of content elements is generated.

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: monitoring first activities of a user over a first time period; based on the first activities of the user over the first time period, identifying, using a Markov model, a first probability of the user being in a first state; determining when the first probability is above a first probability predefined threshold; in response to determining when the first probability is above the first probability predefined threshold, automatically customizing first content on a graphical user interface for the first state to create a first graphical user interface on an electronic device of the user while the user is determined to be in the first state; monitoring second activities of the user over a second time period occurring after the first time period and after the user has been determined to be in the first

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: displaying content elements on one or more websites to users; tracking respective impression response data for each impression of a respective content element of the content elements comprising (a) a respective response of a respective user of the users and (b) a respective time of the respective response of the respective user; determining respective weightings of the content elements based on posterior distributions using the respective impression response data, as adjusted by a temporal decay factor, based on the respective times of the respective impression response data for the content elements; and generating a webpage of the one or more web sites to comprise a selected content element based on the respective weighting of the selected content element. 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: displaying content elements on one or more websites to users; performing a classification of the users into segments; receiving a request from a first user of the users to display a webpage of the one or more websites; determining an amount of information in a respective posterior distribution of each of the one or more first segments of the segments in which the first user is classified, for each of the content elements based on impression response data; selecting a selected content element from among the content elements based on weightings of the content elements for the one or more first segments; and generating the webpage comprising the selected content element. Other embodiments are disclosed.

Abstract: A system and method for content selection and presentation is disclosed. A system receives a plurality of content elements configured for presentation in at least one content container and selects one of the plurality of content elements for presentation in the at least one content container. The one of the plurality of content elements is selected by a trained selection model configured to use Thompson sampling. An interface including the selected one of the plurality of content elements is generated.

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: receiving a query from a front-end device for one or more users mapped to a same payment option; generating, using a machine learning model, a first dataset comprising one or more classifications of one or more online users mapped to the same payment option as either (i) a single user with multiple user profiles or (ii) multiple users of a same household; generating, using a factor graph, a second dataset comprising first information of the one or more online users mapped to second information of one or more instore users; mapping at least one of the one or more online users to at least one of the one or more instore users based on the second dataset. Other embodiments are disclosed.

Abstract: A system and method for content selection and presentation is disclosed. A plurality of content elements configured for presentation in at least one content container is received and one of the plurality of content elements is selected for presentation in the at least one content container. The one of the plurality of content elements is selected by a trained selection model configured to select one of an individual context or a global context. An interface including the selected one of the plurality of content elements is generated.

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 system and method for content selection and presentation is disclosed. A plurality of content elements configured for presentation in at least one content container is received and one of the plurality of content elements is selected for presentation in the at least one content container. The one of the plurality of content elements is selected by a trained selection model configured to select one of an individual context or a global context. An interface including the selected one of the plurality of content elements is generated.