Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transactional data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of trial membership scores, based on the set of features. In some examples, each trial membership score of the plurality of trial membership scores are associated with a particular user of the set of users and characterize a likelihood of an acquisition event of the corresponding user changing from a non-member status to a trial member status.

Abstract: A door latch device for vehicles may configure a pop-up guide unit at a location far from a hinge portion on a door by integrally forming the pop-up guide unit in a latch unit for keeping a locking state of the door in a vehicle without a B pillar, preventing a mutual sweeping phenomenon of a door or vehicle body-side weather strip during a pop-up operation process of the door.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital customer insights based on historical customer data. In some examples, a computing device obtains user data for a user. In response, the computing device receives a plurality of product types relevant to the user and their corresponding relevance scores. For each product type, the computing device then receives a set of attributes, where each attribute is associated with an affinity score for the user. The computing device determines, for each product type, an overall score for each attribute and product type pair based on the relevance score for the product type and the affinity score for the corresponding attribute. At least one attribute and product type pair is presented to the user based on the corresponding overall score.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: A method can include retrieving product information from a website database to identify a first product as a value-sensitive product identified with at least a value price tag. The method can include determining second users who are not value conscious about the first product. The method can also include preparing first and second recommendations and promotions for the first product, wherein the first recommendation comprises one or more value-sensitive products. The method additionally can include transmitting machine readable instructions to display the first recommendations and promotions for the first product for viewing by the first user. The method also can include transmitting machine readable instructions to display the second recommendations and promotions for the first product for viewing by the second user. Other embodiments are disclosed herein.

Abstract: A seasonal recommendation system can include a computing device that is configured to receive a request to identify a set of recommendations associated with a season, obtain historical data over a threshold period for a set of product types, and compute a seasonality index score based on the historical data over a target period and the threshold period. The computing device is also configured to select a subset of product types based on the seasonality index score and by applying a theme-aware model to the product types and identify and store a set of items corresponding to at least one product type of the subset of product types. The computing device is configured to, in response to a user navigating to a webpage using a user device, select and display at least one item of the set of items on a user interface of the user device.

Abstract: A hold-open lock apparatus of a sliding door configured to limit or release rotation of a swing arm without interference with a rail is provided. The hold-open lock apparatus includes a catch unit including a catch pin that serves as a center of rotation of the swing arm, a catch fitted over the catch pin, and a pawl release lever rotatably fitted over the catch pin. The hold-open lock apparatus further includes a pawl unit including a pawl pin located on a side of the swing arm, and a pawl rotatably fitted over the pawl pin and configured to be locked to or unlocked from the catch. The hold-open lock apparatus also includes a cable unit connected to the pawl release lever and configured to rotate the pawl release lever. The pawl release lever is configured to rotate in one direction to rotate the pawl.

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 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 creating a standardized flavor list from flavors associated with items in a catalog; determining a respective score indicating a degree of affinity for a user for each flavor of each of one or more item types associated with the items; creating a respective ordered list of flavors for each of the one or more item types for the user in which the respective ordered list of flavors are ranked by the respective score for the each flavor in the ordered list; and re-ranking a non-personalized list of recommended items associated with an anchor item of the items using the respective ordered list of flavors for the user. Other embodiments are disclosed.

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 for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

Abstract: This application relates to apparatus and methods for automatically determining and providing item advertisements to customers. In some examples, a computing device obtains transaction data identifying in-store and/or online transactions. The computing device determines a distribution of purchased items over a plurality of item categories based on the transaction data. The computing device generates factorization matrices based on applying a machine learning process to the distribution, and generates relevancy scores for the plurality of item categories based on the factorization. The computing device may then select or generate item advertisements for items associated with the item categories based on the generated relevancy scores. The selected item advertisements may be displayed to a customer, for example, on a website.

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 acts of: providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query. Other embodiments are disclosed herein.

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 acts of: generating one or more attribute affinity scores for one or more attributes associated with an item type category, wherein the one or more attribute affinity scores predict a user's affinity for attribute values associated with the one or more attributes; generating a respective attribute importance score for each of the one or more attributes, the respective attribute importance score predicting a respective importance of each of the one or more attributes to the user; and generating personalized search results that are ordered based, at least in part, on the one or more attribute affinity scores and the respective attribute importance scores. Other embodiments are disclosed herein.

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 acts of: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

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 generating personalized product-type metrics for the user based at least in part on a user embedding for the user and product-type embedding Gaussian mixture distributions; determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements; generating a set of first items associated with the top product-types; ranking each respective item in the set of first items generated using an item-level embedding Gaussian distribution for the anchor item and a respective item-level embedding Gaussian distribution for the each respective item; and selecting a set of top items as the personalized complementary item recommendations based on the ranking. Other embodiments are disclosed.

Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

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 acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.

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 creating a standardized flavor list from flavors associated with items in a catalog; determining a respective score indicating a degree of affinity for a user for each flavor of each of one or more item types associated with the items; creating a respective ordered list of flavors for each of the one or more item types for the user in which the respective ordered list of flavors are ranked by the respective score for the each flavor in the ordered list; and re-ranking a non-personalized list of recommended items associated with an anchor item of the items using the respective ordered list of flavors for the user. Other embodiments are disclosed.