Beijing Baidu Netcom Science and Technology Co., Ltd.

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

Beijing Baidu Netcom Science Technology Co., Ltd

Jianguo Duan

Hui Xiong

Yanyan Li

Jingbo Zhou

Hao Liu

Panpan Zhang

Xinjiang Lu

Airong Jiang

Renjun Hu

Guanglei Du

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

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

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 method, device, and medium for recommending a region of interest are provided. The method includes: acquiring access data, the access data including correlation information between any two regions in a region group, in which a correlation between any two regions in the region group is acquired based on a region pair formed by the any two regions in the region group where sample users are located and the number of access times corresponding to the region pair, and in which the region group is acquired based on division of map data, and the map data includes boundary information of an entity in a real world; determining a region where a first user is currently located; and recommending region information of the region of interest for the first user based on the access data and the region where the first user is currently located.

Recommendation of region of interest

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

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.

Method and apparatus for generating information

The present application discloses a method for data aggregation, the method includes: acquiring original data to be aggregated and dividing the original data into at least one first data set; determining whether each of the at least one first data set has a corresponding historical aggregation record; when there is at least one second data set with a historical aggregation record in the at least one first data set, acquiring a historical aggregation result corresponding to each second data set to obtain at least one first aggregation result; performing aggregation on each third data set without a historical aggregation record to obtain at least one second aggregation result; and determining a third aggregation result of the original data according to the at least one first aggregation result and the at least one second aggregation result, and determining a data tag of the original data according to the third aggregation result.

Method, apparatus, device and storage medium for data aggregation

A method, an apparatus, a computer device and a storage medium for stay point recognition are disclosed. The method comprises: obtaining reference stay points corresponding to a to-be-recognized positioning point; selecting from the reference stay points a stay point in a predetermined relationship with the to-be-recognized positioning point; and determining whether the to-be-recognized positioning point is a stay point based on the stay point selected. The technical solution may improve the accuracy and recall rate of the stay point recognition.

Method, apparatus, computer device and storage medium for stay point recognition

A method, an apparatus and a storage medium for stay point recognition and prediction model training are proposed. The method may include: for a to-be-recognized positioning point, obtaining respectively features of N predetermined dimensions for the to-be-recognized positioning point, N being a positive integer greater than one; generating a feature vector for the to-be-recognized positioning point according to the features obtained; determining whether the to-be-recognized positioning point is a stay point based on the feature vector, by using a prediction model obtained by pre-training. The technical solution may be applied to improve the accuracy and a recall rate of the stay point recognition.

Method, apparatus and storage medium for stay point recognition and prediction model training

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.

Method, computer device and storage medium for mining point of interest competitive relationship

Disclosed are a method and apparatus for generating information. A method may include: acquiring a visiting data sequence and a positioning data sequence of a user, visiting data including visiting time and a point of interest identifier, and positioning data including positioning time and location information; using, in response to determining that the visiting time of the visiting data in the visiting data sequence matches the positioning time of the positioning data in the positioning data sequence, the visiting data corresponding to the matched visiting time as target visiting data, and the positioning data corresponding to the matched positioning time as target positioning data; determining an effective area of a target point of interest; and determining a dwell time of the user at the target point of interest based on the effective area and the positioning data sequence.

Disclosed are a method and apparatus for generating information. A method may include: receiving wireless local area network scanning information sent by a terminal, the wireless local area network scanning information including a wireless local area network identifier of at least one wireless local area network; determining, from the wireless local area network identifier of the at least one wireless local area network, a wireless local area network identifier as a target wireless local area network identifier; determining feature information of a target wireless local area network corresponding to the target wireless local area network identifier; and generating, based on the feature information of the target wireless local area network and a pre-established category identification model, category information corresponding to the target wireless local area network, the category identification model being used to represent a corresponding relationship between the feature information and the category information.

A method, a computer device and a storage medium for matching a mobile hotspot with a mobile point of interest are proposed. The method includes: dividing a spatial region into a plurality of grid cells of a predetermined size, and generating an index for each of the plurality of grid cells; for each mobile POI of a plurality of mobile POIs in the spatial region, determining respectively indexes of grid cells through which a transport route of the mobile POI passes, to obtain a first index encoding result of the mobile POI, the mobile POI being a transport vehicle having a fixed commuting route; for a mobile hotspot in the spatial region, obtaining a movement trajectory of the mobile hotspot, determining respectively indexes of grid cells through which the movement trajectory passes, to obtaining a second index encoding result of the mobile hotspot; matching the mobile hotspot with a mobile POI of the plurality of mobile POIs according to a similarity between the first index encoding result and the second index encoding result. The technical solutions of the present disclosure may be applied to reduce the implementation cost and improve the processing efficiency and the accuracy of the processing result.

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Beijing, China

Jianguo Duan