A system and method for placement optimization of public electric vehicle charging stations using telematics data that includes receiving vehicle telematics data from a plurality of vehicles. The system and method also includes analyzing the vehicle telematics data to determine clusters of candidate locations of the public electric vehicle charging stations and selecting a subset of nodes of a fully connected graph structure that are associated with the candidate locations as optimal locations of the public electric vehicle charging stations. The system and method further include controlling an electronic computing system to present a human machine interface to present a visualization of the optimal locations of public electric vehicle charging stations to at least one party.

Embodiments of the disclosure provide systems and methods for blocking a ride-hailing order. The method may include receiving incident data associated with a plurality of incidents. The incident data indicates a time and a location of each incident. The method may also include determining, by a processor, an area encompassing the locations of the plurality of incidents. The method may further include determining, by the processor, a plurality of time intervals each encompassing the time of at least one incident. The method may further include determining, by the processor, a time slot by merging the plurality of time intervals based on a time gap between every two neighboring time intervals. The method may further include designating, by the processor, a combination of the area and the time slot as a block zone and blocking an order generated from the area during the time slot of the block zone.

A device may receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users, and may preprocess the purchase data to generate sequences of multivariate and multimodal symbols. The device may process the sequences of multivariate and multimodal symbols, with a long short-term memory based encoder-decoder model, to generate sequence embeddings, and may process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data. The device may process the sequence embeddings and the knowledge graph embeddings, with a knowledge graph embedding model, to generate modified sequence embeddings, and may process the modified sequence embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users. The device may perform one or more actions based on the clusters of the users.

Concepts may be associated with each other based on information provided by data sources. Entities may be associated based on the information provided by the data sources and characteristics of the entities. A concept graph may be generated based on the concepts such that each edge in the concept graph corresponds to a relationship between two or more associated concepts. A data graph may be generated based on the concept graph and the entities such that each node in the data graph corresponds to a concept or an entity and the edges in the data graph correspond to relationships between two or more concepts and such that other relationships between two or more associated concepts are absent from the concept graph. In response to a query, traversing the data graph to determine entities that are related to the query and providing a report that includes those entities.

The present invention discloses a method, device and browser for presenting recommended news, and an electronic device. Said method for presenting recommended news comprises: displaying a map; displaying recommended news tags on the map; receiving an operation of a user carried out in respect of the recommended news; and displaying a corresponding recommended news list. A new method for presenting recommended news is provided according to the embodiments of the present invention.

Technologies are generally described for context sensitive display of final delivery information on a consumable delivery vehicle with enroute preparation. A controller may manage fulfillment of orders while the vehicle is enroute by controlling an operation of one or more on-board preparation equipment. The controller may also receive order status information and travel information associated with final delivery destinations. Once the vehicle is at its destination to be used as a hub for final deliveries, one or more displays on the vehicle may display final delivery information to final delivery people based on the status information and the travel information.

Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

A method may include generating a feature table, hierarchical segments, and a graph network based on raw interaction data of a set of users. The method may further include generating a set of rankings for features in the feature table. The method may further include targeting hierarchical segments of the set of users through marketing campaigns and calculate a set of elasticity scores for the set of users in response to the marketing campaigns in the hierarchical segments. The method may further include generating item recommendations for the set of users based on the graph network. The method may further include executing a machine learning model to generate an uplift score for each user from the set of users based on at least one of the raw interaction data, the set of rankings, hierarchical segments, the set of elasticity scores, or the item recommendations.

A computer-implemented method that includes receiving a request for a target vehicle including input data associated with an operator, a geographic location, and the target vehicle. The method includes determining a depreciation rate based on third-party transaction data retrieved from databases in accordance with the input data associated with the target vehicle, and a decommission rate based on third-party ownership data retrieved from databases in accordance with the input data associated with the target vehicle. The method includes determining a deterioration rate based on local environmental data retrieved from databases in accordance with the input data associated with the geographic location, and generating a regression model configured to compute an estimated value of the target vehicle during a lifetime of the target vehicle using the input data, depreciation rate, decommission rate, and deterioration rate. The method includes determining an output in response to the request for the target vehicle.

Familial considerations are a key component in making decisions related to buying and selling of real estate such as primary residences. In this system these considerations are represented using the definition of a family score based on criteria which range from the needs of self, spouse partner, parents, children, other family and friends.

We recommend using a deep learning and machine learning-based approach to determine the family score. The family score is the weighted score determined by the current and evolving needs of family and events which determine property transaction decisions. The system will determine the family score for each property in-order to provide the suitability of the property for a given family. The considerations for mapping properties and families will depend on the attributes of the property and the requirements of the family.