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: 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: 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: A method and apparatus for generating information. A specific embodiment of the method includes: acquiring a set of geographic information point sequences and a set of identifiers comprising an identifier of each geographic information point sequence in the set of the geographic information point sequences; for the each geographic information point sequence in the set of the geographic information point sequences, clustering geographic information points in the each geographic information point sequence and generating an element sequence corresponding to the each geographic information point sequence; learning, by utilizing a machine learning method based on the set of identifiers and the generated element sequences, to obtain a matrix for the identifiers in the set of identifiers; and generating, for each identifier in the set of identifiers, based on the identifier and the matrix, information of a user to which the geographic information point sequence indicated by the identifier belongs.

Abstract: A method for rapidly preparing a Sanger sequencing template, comprising: performing denaturing treatment on a sample containing circular DNAs; performing rolling circle amplification of the denaturing product; and treating the rolling circle amplification product with alkaline phosphatase to remove residual dNTP.

Abstract: The present application provides a method for quickly homogenizing circular DNA samples, comprising performing rolling circle amplification on circular DNAs in the samples, so that the concentration of the circular DNAs in the samples are homogenized.

Abstract: Embodiments of the present disclosure provide a method and apparatus for optimizing a tag of a point of interest. The method includes: acquiring a set of points of interest and a set of tags of points of interest in the set of points of interest; generating a point of interest-tag matrix based on the set of points of interest and the set of tags of points of interest in the set of points of interest; extracting a feature of a point of interest-tag in the point of interest-tag matrix; inputting the feature of the point of interest-tag in the point of interest-tag matrix into a pre-trained ternary self-adaptive collaborative learning model, to obtain a point of interest-tag score matrix; and optimizing the set of tags of points of interest in the set of points of interest based on the point of interest-tag score matrix.

Abstract: The present disclosure provides a method and apparatus for building a text classification model, and a text classification method and apparatus. The method of building a text classification model comprises: obtaining a training sample; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; using the vector matrix corresponding to the text and a class of the text to train a first classification model and a second classification model respectively; during the training process, using a loss function of the first classification model and a loss function of the second classification model to obtain a loss function of the text classification model, and using the loss function of the text classification model to adjust parameters for the first classification model and the second classification model, to obtain the text classification model formed by the first classification model and the second classification model.

Abstract: A road detection method and apparatus. A specific embodiment of the method includes: acquiring an image of a predetermined region; semantically segmenting the image to acquire a first probability that a region corresponding to each pixel in the image is a road region; acquiring a historical position information set of a target terminal; correcting, in response to historical position information existing in the historical position information set, the historical position information indicating a historical position located in the predetermined region, the first probability according to the historical position information to obtain a second probability; and determining a region corresponding to a pixel having the second probability greater than a preset threshold as a road region. Such an embodiment improves the road detection accuracy.

Abstract: 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.

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: A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.

Abstract: A method and apparatus for pushing information. A specific embodiment of the searching method comprises: analysing, based on a second search operation sequentially performed to a map application installed on a client by a user, a search term corresponding to the second search operation; matching the analysed search term with a first search term in a first search term sequence to determine whether the matching is successful; acquiring log data of the map application installed on the client, in response to the matching being successful; determining a vehicle demand probability of the user based on the log data, acquired feature information of the user and a pre-trained vehicle demand probability model; and pushing information to the client in response to the determined vehicle demand probability being greater than a preset vehicle demand probability threshold.

Abstract: A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.

Abstract: The present disclosure provides a method and apparatus for building a text classification model, and a text classification method and apparatus. The method of building a text classification model comprises: obtaining a training sample; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; using the vector matrix corresponding to the text and a class of the text to train a first classification model and a second classification model respectively; during the training process, using a loss function of the first classification model and a loss function of the second classification model to obtain a loss function of the text classification model, and using the loss function of the text classification model to adjust parameters for the first classification model and the second classification model, to obtain the text classification model formed by the first classification model and the second classification model.

Abstract: A road detection method and apparatus. A specific embodiment of the method includes: acquiring an image of a predetermined region; semantically segmenting the image to acquire a first probability that a region corresponding to each pixel in the image is a road region; acquiring a historical position information set of a target terminal; correcting, in response to historical position information existing in the historical position information set, the historical position information indicating a historical position located in the predetermined region, the first probability according to the historical position information to obtain a second probability; and determining a region corresponding to a pixel having the second probability greater than a preset threshold as a road region. Such an embodiment improves the road detection accuracy.

Abstract: A method and apparatus for generating information. A specific embodiment of the method includes: acquiring a set of geographic information point sequences and a set of identifiers comprising an identifier of each geographic information point sequence in the set of the geographic information point sequences; for the each geographic information point sequence in the set of the geographic information point sequences, clustering geographic information points in the each geographic information point sequence and generating an element sequence corresponding to the each geographic information point sequence; learning, by utilizing a machine learning method based on the set of identifiers and the generated element sequences, to obtain a matrix for the identifiers in the set of identifiers; and generating, for each identifier in the set of identifiers, based on the identifier and the matrix, information of a user to which the geographic information point sequence indicated by the identifier belongs.

Abstract: A method and apparatus for pushing information. A specific embodiment of the searching method comprises: analysing, based on a second search operation sequentially performed to a map application installed on a client by a user, a search term corresponding to the second search operation; matching the analysed search term with a first search term in a first search term sequence to determine whether the matching is successful; acquiring log data of the map application installed on the client, in response to the matching being successful; determining a vehicle demand probability of the user based on the log data, acquired feature information of the user and a pre-trained vehicle demand probability model; and pushing information to the client in response to the determined vehicle demand probability being greater than a preset vehicle demand probability threshold.