Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Other

Hui Xiong

Jianguo Duan

Yanyan Li

Hao Liu

Jingbo Zhou

Hengshu Zhu

Weijia Zhang

Renjun Hu

Jie Yan

Yaolong Zhu

Panpan Zhang

Chuan Qin

Tong Xu

Airong Jiang

Xinjiang Lu

Chao Wang

Qi Zhang

Ting Li

Dejing Dou

Yaqing Wang

Zheng Dong

Chen Zhu

Dazhong Shen

Guanglei Du

Yanjie Fu

Shuangli Li

Nengjun Zhu

A method for determining a text topic includes: after a word sequence corresponding to a text to be processed and a number of spaced words in the text to be processed between each two words in the word sequence are determined, a graph structure corresponding to the text to be processed may be determined based on the number of spaced words between each two words in the text to be processed, a topic distribution corresponding to the text may be determined based on the word sequence and the graph structure, a topic corresponding to the text may be determined based on the topic distribution.

Method for determining text topic, and electronic device

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.

Method and apparatus for generating recommendation model, content recommendation method and apparatus, device and medium

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.

Station recommendation

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.

Method and apparatus for optimizing tag of point of interest, electronic device and computer readable medium

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.

Resident area prediction method, apparatus, device, and storage medium

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.

Cross-regional travel recommendation method and apparatus, electronic device and storage medium

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.

Method for determining competitive relation of points of interest, device

The present disclosure discloses a method, an apparatus, a device, and a storage medium for determining a point of interest area, and relates to the field of automatic driving. The implementation solution is that the method is applied to an electronic device, and includes: receiving a point of interest area determination request input by a first user, the point of interest area determination request including a target area coverage; and acquiring grid data of at least one block within the target area coverage in response to the point of interest area determination request; acquiring, for each block, positioning data of a second user within each preset time period and number of parent points of interest; clustering corresponding grid data according to the positioning data, the grid data and the number of the parent points of interest; determining at least one POI area in each block according to a clustering result.

Method, apparatus, device and storage medium for determining point of interest area

Embodiments of the present disclosure provide a method, apparatus, computer device, and storage medium for determining a POI alias. The method may include: acquiring a to-be-processed target POI, and generating a candidate alias list corresponding to the target POI based on a query behavior-associated log matching the target POI; and screening out at least one target alias corresponding to the target POI in the candidate alias list, according to an association relationship between each candidate alias in the candidate alias list and the target POI.

Method, apparatus, computer device and storage medium for determining POI alias

A parking lot free parking space predicting method, apparatus, electronic device and storage medium are provided. The method comprises: building a parking lot association graph for parking lots in a region to be processed; aggregating environment context features of neighboring parking lots according to weights of edges between the neighboring parking lots and a parking lot i to obtain a representation vector of the parking lot i at a current time; and pre-training a graph attention neural network model using the environment context features of the neighboring parking lots and free parking space information, and a gated recurrent neural model according to the representation vector of the parking lot it at the current time.

Parking lot free parking space predicting method, apparatus, electronic device and storage medium

Embodiments of the present disclosure provide a travel plan recommendation method, an apparatus, a device and a computer readable storage medium. In the method according to the embodiments of the present disclosure, the travel plan classification model is obtained by training with the comprehensive consideration of the diversity of users, the diversity of the travel environment in time and space and dynamics of the user's travel preference, and the travel feature vector is generated according to the user persona information and the travel mode distribution information of the user, the current travel environment feature information and the feature information of the starting point and the destination point of the current travel, the travel feature vector is inputted into the travel plan classification model to calculate the scores of the candidate travel plans, and the travel plan recommended to the user according to the scores of the candidate travel plans.

Travel plan recommendation method, apparatus, device and computer readable storage medium

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. The method for generating a user intention understanding satisfaction evaluation model is: acquiring a plurality of sets of intention understanding data, at least one set of which comprises a plurality of sequences corresponding to multi-round behaviors of an intelligent device in multi-round man-machine interactions; and learning the plurality of sets of intention understanding data through a first machine learning model, to obtain the user intention understanding satisfaction evaluation model after the learning, wherein the user intention understanding satisfaction evaluation model is configured to evaluate user intention understanding satisfactions of the intelligent device in the multi-round man-machine interactions according to the plurality of sequences corresponding to the multi-round man-machine interactions.

Method and apparatus for evaluating user intention understanding satisfaction, electronic device and storage medium

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 encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing 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.

Method and apparatus for mining competition relationship POIs

A parking lot free parking space predicting method, apparatus, electronic device and storage medium are provided. The method comprises: building a parking lot association graph for parking lots in a region to be processed; as for any parking lot i, performing the following processing respectively: determining local space correlation information of the parking lot i at a current time according to environment context features of the parking lot i and neighboring parking lots which are in the parking lot association graph and connected to the parking lot i through edges; determining time correlation information of the parking lot i at the current time according to the local space correlation information, and predicting free parking space information of the parking lot i at at least one future time step according to the time correlation information of the parking lot i at the current time.

Embodiments of the present disclosure provide a method and apparatus for generating information. A method may include: determining, according to received positioning request information, visiting information for a target area of interest, the visiting information including location information of at least one visiting point; determining, according to the location information of the at least one visiting point, a visiting point distribution map including the at least one visiting point; performing grid division on the visiting point distribution map, to obtain a first grid map including at least one grid; and generating, based on the first grid map, outline information for the target area of interest.

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Beijing, China

Hui Xiong