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

An apparatus for determining a point of interest (POI) alias, the apparatus comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, causing the at least one processor to perform operations, the operations comprising:
• acquiring a target POI that is to be processed, and generating a candidate alias list corresponding to the target POI based on a query behavior-associated log, the query behavior-associated log being used for recording data of query behavior directing at the target POI by a network user in an application providing location-based services (LBS), matching the target POI;
• and
• screening out at least one target alias corresponding to the target POI in the candidate alias list, based on association relationships between candidate aliases in the candidate alias list and the target POI,
• wherein before screening out at least one target alias corresponding to the target POI in the candidate alias list, the operations further comprise:
• extracting a sequence comprising at least one candidate alias and a standard name of the target POI from the query behavior-associated log, and building at least one alias-name pair from the sequence, the alias-name pair comprising a candidate alias and the standard name;
• and
• eliminating a candidate alias consistent with the standard name of the target POI from the candidate alias list;
• or eliminating a candidate alias from the candidate alias list in response to a number of the alias-name pair of the candidate alias being less than a set number threshold, and
• wherein the screening out at least one target alias corresponding to the target POI in the candidate alias list, according to association relationships between candidate aliases in the candidate alias list and the target POI, comprises:
• acquiring at least one alias description feature corresponding to a candidate alias in the candidate alias list;
• inputting the at least one alias description feature of the candidate alias into a pre-trained POI alias identification model, and acquiring an alias probability corresponding to the candidate alias output by the POI alias identification model;
• and
• screening out the at least one target alias in the candidate alias list based on alias probabilities of the candidate aliases,
• the pre-trained POI alias identification model being trained based on at least one piece of standard sample data obtained by:
• based on query behavior-associated logs corresponding to a location service application, extracting a piece of sample group data comprising a user query formula, a user clicked POI, and a user actually visited POI for each query behavior-associated log, to obtain a plurality of pieces of sample group data corresponding to the query behavior-associated logs;
• for each piece of sample group data, determining whether a user query formula thereof is different from a user clicked POI thereof and the user clicked POI thereof is identical with a user actually visited POI thereof, and determining the piece of piece target sample group to be a sample group in response to the user query formula thereof being different from a user clicked POI thereof and the user clicked POI thereof being identical with a user actually visited POI thereof;
• constructing a piece of candidate sample data with the user query formula comprised in the target sample group as a candidate alias and a standard name of the user clicked POI as a standard name of a candidate sample POI;
• acquiring word vectors of a candidate alias and the candidate sample POI of a piece of candidate sample data, and calculating a vector similarity between the candidate alias and the standard name of the corresponding candidate sample POI;
• and
• screening out the standard sample data in the candidate sample data based on the vector similarities corresponding to respective pieces the candidate sample data.

A non-transitory storage medium, storing a computer program thereon, wherein the program, when executed by a processor, cause operations to be performed, the operations comprising:

• acquiring a target point of interest (POI) that is to be processed, and generating a candidate alias list corresponding to the target POI based on a query behavior-associated log, the query behavior-associated log being used for recording data of query behavior directing at the target POI by a network user in an application providing location-based services (LBS), matching the target POI;
• and
• screening out at least one target alias corresponding to the target POI in the candidate alias list, based on association relationships between candidate aliases in the candidate alias list and the target POI,
• wherein before screening out at least one target alias corresponding to the target POI in the candidate alias list, the operations further comprise:
• extracting a sequence comprising at least one candidate alias and a standard name of the target POI from the query behavior-associated log, and building at least one alias-name pair from the sequence, the alias-name pair comprising a candidate alias and the standard name;
• and
• eliminating a candidate alias consistent with the standard name of the target POI from the candidate alias list;
• or eliminating a candidate alias from the candidate alias list in response to a number of the alias-name pair of the candidate alias being less than a set number threshold, and
• wherein the screening out at least one target alias corresponding to the target POI in the candidate alias list, according to association relationships between candidate aliases in the candidate alias list and the target POI, comprises:
• acquiring at least one alias description feature corresponding to a candidate alias in the candidate alias list;
• inputting the at least one alias description feature of the candidate alias into a pre-trained POI alias identification model, and acquiring an alias probability corresponding to the candidate alias output by the POI alias identification model;
• and
• screening out the at least one target alias in the candidate alias list based on alias probabilities of the candidate aliases,
• the pre-trained POI alias identification model being trained based on at least one piece of standard sample data obtained by:
• based on query behavior-associated logs corresponding to a location service application, extracting a piece of sample group data comprising a user query formula, a user clicked POI, and a user actually visited POI for each query behavior-associated log, to obtain a plurality of pieces of sample group data corresponding to the query behavior-associated logs;
• for each piece of sample group data, determining whether a user query formula thereof is different from a user clicked POI thereof and the user clicked POI thereof is identical with a user actually visited POI thereof, and determining the piece of piece target sample group to be a sample group in response to the user query formula thereof being different from a user clicked POI thereof and the user clicked POI thereof being identical with a user actually visited POI thereof;
• constructing a piece of candidate sample data with the user query formula comprised in the target sample group as a candidate alias and a standard name of the user clicked POI as a standard name of a candidate sample POI;
• acquiring word vectors of a candidate alias and the candidate sample POI of a piece of candidate sample data, and calculating a vector similarity between the candidate alias and the standard name of the corresponding candidate sample POI;
• and
• screening out the standard sample data in the candidate sample data based on the vector similarities corresponding to respective pieces the candidate sample data.