Method and apparatus for determining search results, device and computer storage medium

A method of training a search result sorting model, wherein the method comprises:

• using a search log to obtain a training sample, the training sample comprising a sample query, a historical query sequence of the sample query, candidate search results of the sample query, and click situations of the candidate search results;
• regarding the sample query, the historical query sequence of the sample query and the candidate search results of the sample query as input of a sorting model, regarding scores of the candidate search results rendered by the sorting model as output, and training the sorting model to achieve a preset training target;
• wherein the scores rendered by the sorting model for the candidate search results are determined according to a similarity degree between an integrated vector representation of the sample query and the historical query sequence of the sample query and vector representations of the candidate search results of the sample query;
• the training target includes:
• maximizing relevancy between click situations of the candidate search results and scores of candidate search results;
• using the sorting model obtained from the training to obtain a search result-sorting model.

A device, wherein the device comprises:

• one or more processors;
• a storage for storing one or more programs;
• the one or more programs, when executed by said one or more processors, enable said one or more processors to implement a method of determining search results, wherein the method comprises:
• obtaining a current query, a historical query sequence of the current query and candidate search results of the current query;
• inputting the current query, the historical query sequence of the current query and candidate search results of the current query into a search result-sorting model, scoring the candidate search results according to the search result-sorting model, and determining search results corresponding to the current query;
• wherein the scores rendered by the search result-sorting model for the candidate search results are determined according to a similarity degree between an integrated vector representation of the current query and the historical query sequence of the current query and vector representations of the candidate search results.

A device, wherein the device comprises:

• one or more processors;
• a storage for storing one or more programs;
• the one or more programs, when executed by said one or more processors, enable said one or more processors to implement a method of training a search result sorting model, wherein the method comprises:
• using a search log to obtain a training sample, the training sample comprising a sample query, a historical query sequence of the sample query, candidate search results of the sample query, and click situations of the candidate search results;
• regarding the sample query, the historical query sequence of the sample query and the candidate search results of the sample query as input of a sorting model, regarding scores of the candidate search results rendered by the sorting model as output, and training the sorting model to achieve a preset training target;
• wherein the scores rendered by the sorting model for the candidate search results are determined according to a similarity degree between an integrated vector representation of the sample query and the historical query sequence of the sample query and vector representations of the candidate search results of the sample query;
• the training target includes:
• maximizing relevancy between click situations of the candidate search results and scores of candidate search results;
• using the sorting model obtained from the training to obtain a search result-sorting model.

A storage medium including computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, implement a method of training a search result sorting model, wherein the method comprises:

• using a search log to obtain a training sample, the training sample comprising a sample query, a historical query sequence of the sample query, candidate search results of the sample query, and click situations of the candidate search results;
• regarding the sample query, the historical query sequence of the sample query and the candidate search results of the sample query as input of a sorting model, regarding scores of the candidate search results rendered by the sorting model as output, and training the sorting model to achieve a preset training target;
• wherein the scores rendered by the sorting model for the candidate search results are determined according to a similarity degree between an integrated vector representation of the sample query and the historical query sequence of the sample query and vector representations of the candidate search results of the sample query;
• the training target includes:
• maximizing relevancy between click situations of the candidate search results and scores of candidate search results;
• using the sorting model obtained from the training to obtain a search result-sorting model.