Abstract: A training method for a point-of-interest recommendation model and a method for recommending a point of interest are provided.

Abstract: A travel recommendation method, an electronic device, and a storage medium are provided, which are related to artificial intelligence, and particularly relates to fields of depth learning, map navigation and the like. The specific implementation scheme includes: obtaining a travel recommendation model according to constraint conditions and prediction conditions, wherein the constraint conditions are used for characterizing travel fairness for different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users; and obtaining travel recommendation information according to a travel target and the travel recommendation model.

Abstract: A method for recommending a station for a vehicle, a device, and a storage medium are provided. The method comprises: receiving, by a server, an access request from a vehicle; obtaining, based on the access request, a plurality of observation values from a plurality of stations associated with the vehicle, respectively, each observation value is based on a corresponding pre-trained recommendation model, each observation value includes factors associated with access of the vehicle to the station corresponding to the observation value; determining, an action value for the station based on the observation value and the pre-trained recommendation model for the station, the action value for the station indicates a matching degree between the access request and the station; determining a recommended station among the plurality of stations based on the action values of the plurality of stations; and sending to the vehicle an instruction of driving to the recommended station.

Abstract: A method for determining a competitive relation of Points of Interest (POI), and a device are provided in the present disclosure. The specific implementation includes: determining POI representation data between two target POIs based on service-related data of the target POIs; and determining a competitive relation between the target POIs based on the POI representation data.

Abstract: Embodiments of the disclosure provide a method and an apparatus for talent-post matching, a device and a medium, which relates to the field of information matching and recruitment. The method includes: determining a resume feature representing a resume to be predicted according to a post submitted and the resume to be predicted; and predicting a matching degree between the resume to be predicted and the post by using a pre-trained predictive model according to a post feature of the post and the resume feature. With the method and the apparatus for talent-post matching, the device and the medium provided in embodiments of the present disclosure, the resume and the post may be matched automatically.

Abstract: A method, computer device and storage medium for mining a point of interest competitive relationship are disclosed. The method includes: for a first POI to be processed, obtaining a set of second POIs serving as mining objects of the first POI; for each second POI in the set, forming a POI pair with the second POI and the first POI, determining a relationship evaluation index of the POI pair according to user's search operations on a map for POIs, and judging whether the two POIs in the POI pair are in a competitive relationship according to the relationship evaluation index. The technical solution of the present disclosure may be applied to improve the accuracy of the processing results.

Abstract: The present disclosure provides a method for generating a recommendation model, a content recommendation method, and a content recommendation apparatus, and an electronic device, and relates to an artificial intelligence field and a deep learning field. The method for generating a recommendation model includes: obtaining a graph training sample set; inputting the graph training sample set into a machine learning model to train the machine learning model, in which the machine learning model includes at least one low-rank graph convolutional network, and the low-rank graph convolutional network includes a complete weight matrix composed of a first low-rank matrix and a second low-rank matrix; in which a training objective of the low-rank graph convolutional network includes a first parameter item, a second parameter item and a non-convex low-rank item; and in responding to detecting that a training end condition is met, determining the machine learning model as a recommendation model.

Abstract: A cross-regional travel recommendation method and apparatus, an electronic device and a storage medium are provided, which relates to the fields of intelligent transportation and deep learning. A specific implementation solution is: acquiring a travel request of a user, the travel request comprising a start point and an end point which are located in different regions; extracting user features according to the travel request of the user; and recommending at least one travel plan to the user according to the user features and a pre-trained cross-regional travel recommendation model. The technical solutions can make up for the deficiency of the existing technology, provide a cross-regional travel plan recommendation under a large-space scale and a multimodal environment through a pre-trained cross-regional travel recommendation model and user features extracted based on a travel request of a user, and can satisfy a cross-regional travel request of the user, and is highly practical.

Abstract: A method and apparatus for generating a user intention understanding satisfaction evaluation model, a method and apparatus for evaluating a user intention understanding satisfaction, an electronic device and a storage medium are provided, relating to intelligent voice recognition and knowledge graphs.

Abstract: Provided are an intelligent recommendation method and apparatus, a model training method and apparatus, an electronic device, and a storage medium, which relate to artificial intelligence technologies, and are applicable to the intelligent recommendation and the intelligent transportation technologies. The intelligent recommendation method includes: determining an object recommendation request; determining, according to a multi-agent strategy model and the object recommendation request, object execution actions of at least two agent objects matching the object recommendation request; determining a target object execution action according to the object execution actions; and recommending the object recommendation request to a target agent object corresponding to the target object execution action.

Abstract: A method for recommending a point of interest (POI) includes: generating a user explicit feature based on a user profile of a user to be recommended; generating a POI explicit feature based on a POI profile of each candidate POI in a pre-constructed POI hierarchical structure; generating a historical interaction feature based on historical interaction behaviors of the user to be recommended to each candidate POI; determining a matrix of recommending values for each hierarchy based on at least one of the user explicit feature, the POI explicit feature and the historical interaction feature in combination with an association relationship between inter-hierarchy candidate POIs and/or intra-hierarchy candidate POIs in the POI hierarchical structure; and selecting at least one target POI from the candidate POIs of each hierarchy based on the matrix of recommending values for each hierarchy.

Abstract: Systems, methods, and compositions are disclosed for a Li-substituted layered-tunneled O3/spinel Na(NixFeyMnz)O2 cathode material, Na0.87Li0.25Ni0.4Fe0.2Mn0.4O2+? (LS-NFM) for enhanced sodium ion storage and cycling stability. The LS-NFM electrode is prepared by adjusting the stoichiometric ratio of the Na ion over the sum of Li and transition metal ions below 1. The Rietveld refinement of XRD data indicates that the cathode is composed of 94% layered and 6% spinel components. When cycled at a high current density of 100 mA g?1, LS-NFM cathode exhibited a first-cycle Coulombic efficiency of 88% and reversible discharge capacity of 107 mAh g?1 after 50 cycles with the capacity retention of 95%.

Abstract: A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which comprises attribute information of retrieved target POIs, the graphlet mining result comprising occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

Abstract: Disclosed are training method and apparatus of a point-of-interest POI recommendation model and an electronic device, relating to the technical fields of artificial intelligence and big data. A specific implementation solution is as follows: when training and generating the POI recommendation model, it is precisely because it is considered that preference information of a user on a POI and a relationship between POIs at different levels will affect the accuracy of a POI recommendation, so when training and generating the POI recommendation model, the preference information of the user on the POI and the relationship between the POIs at different levels are obtained first, and the POI recommendation model is trained and generated according to the preference information of the user on the POI and the relationship between the POIs at different levels, thereby improving the accuracy of the POI recommendation model.

Abstract: The present disclosure provides a method for optimizing a tag of a point of interest s(POI). The method includes: obtaining first portrait feature data of each POI in a plurality of POIs and second portrait feature data of each tag in a plurality of marked tags corresponding to the plurality of POIs; mapping the first portrait feature data of each POI and the second portrait feature data of each tag to a metric space to obtain a first feature vector of each POI and a second feature vector of each tag; and optimizing at least one marked tag corresponding to a target POI based on a vector similarity between a first feature vector of the target POI and a second feature vector of at least one tag. The present disclosure provides an apparatus for optimizing a tag of a POI, an electronic device and a computer readable medium.

Abstract: The present disclosure provides a parking lot free parking space predicting method and apparatus etc., and relates to the field of artificial intelligence.

Abstract: A parking lot free parking space predicting method, apparatus, electronic device and storage medium are provided.

Abstract: A parking lot free parking space predicting method, apparatus, electronic device and storage medium are provided.

Abstract: A land usage property identification method, apparatus, electronic device and storage medium are disclosed. The method includes: acquiring point-of-interest (POI) data and area-of-interest (AOI) data; dividing a target area to be identified according to road network information, and obtaining at least one block in the target area; associating acquired POI data to a corresponding target block in the at least one block; obtaining a first weight set corresponding to a corresponding category of each POI data in the target block; obtaining a second weight set corresponding to a corresponding area of each AOI data in the target block; obtaining a land usage property weight set according to the first weight set, the second weight set and a preset land usage classification standard; and identifying a land usage property of the target block according to a target weight in the land usage property weight set.

Abstract: This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.