CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to Chinese Patent Application No. CN201810194909.3, filed on Mar. 9, 2018, which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of shopping technology in a shopping mall or supermarket, and more particularly, to a shopping recommendation method, a client, and a server.

BACKGROUND

On the one hand, current shopping recommendation solutions are usually only presented in online scenes such as shopping pages etc. Generally, only unified advertisements are placed offline, and it is difficult to make reasonable personalized recommendations for users in offline scenes such as shopping in a shopping mall or supermarket, etc., to improve a purchase rate of the shopping in the shopping mall or supermarket. On the other hand, prediction and recommendation are usually performed only according to users' personal information, shopping history records, chat records of instant messaging tools, etc., and current demands of the users in an offline shopping scene cannot be accurately predicted.

SUMMARY

In a first aspect of the present disclosure, there is provided a shopping recommendation method applicable to a client, comprising:

acquiring voice information of a user, and performing voice recognition and keyword extraction on the voice information, to obtain shopping keyword information;

transmitting the shopping keyword information to a server, so that the server performs interest prediction according to the shopping keyword information and shopping related information of the user, to generate recommendation information; and

receiving the recommendation information, and generating shopping recommendation content according to the recommendation information to be displayed by a display.

In an embodiment, performing the interest prediction comprises:

extracting a feature vector according to the shopping keyword information and the shopping related information;

inputting the feature vector to a deep neural network model for training and prediction to obtain a prediction result; and

generating the recommendation information according to the prediction result.

In an embodiment, the method further comprises:

in a case where no voice information is acquired or no shopping keyword information is extracted, transmitting notification information to the server to notify the server to perform interest prediction according to the shopping related information of the user, to generate the recommendation information.

In an embodiment, the shopping keyword information comprises at least one of commodity category information, commodity name information, commodity rating information, and

the shopping related information comprises at least one of basic information of the user, commodity information, shopping history information, and other interactive information.

In a second aspect of the present disclosure, there is provided a shopping recommendation method applicable to a server, comprising:

receiving shopping keyword information transmitted by a client, wherein the shopping keyword information is obtained by acquiring voice information of a user and performing voice recognition and keyword extraction on the voice information by the client;

performing interest prediction according to the shopping keyword information and shopping related information of the user to generate recommendation information; and

transmitting the recommendation information to the client, so that the client generates shopping recommendation content according to the recommendation information to be displayed by a display.

In an embodiment, performing interest prediction according to the shopping keyword information and shopping related information of the user to generate recommendation information comprises:

extracting a feature vector according to the shopping keyword information and the shopping related information;

inputting the feature vector to a deep neural network model for training and prediction to obtain a prediction result; and

generating the recommendation information according to the prediction result.

In an embodiment, the method further comprises:

receiving notification information transmitted by the client, wherein the notification information is transmitted by the client when no voice information is acquired or no shopping keyword information is extracted; and

after receiving the notification information, performing interest prediction according to the shopping related information of the user, to generate the recommendation information.

In an embodiment, the shopping keyword information comprises at least one of commodity category information, commodity name information, and commodity rating information, and

the shopping related information comprises at least one of basic information of the user, commodity information, shopping history information, and other interactive information.

In a third aspect of the present disclosure, there is provided a client, comprising:

one or more processors;

a memory connected to the one or more processors and having one or more programs stored therein, which, when executed by the one or more processors, cause the one or more processors to be configured to:

acquire voice information of a user, and perform voice recognition and keyword extraction on the voice information, to obtain shopping keyword information;

transmit the shopping keyword information to a server, so that the server performs interest prediction according to the shopping keyword information and shopping related information of the user, to generate recommendation information; and

receive the recommendation information, and generate shopping recommendation content according to the recommendation information to be displayed by a display.

In an embodiment, performing the interest prediction comprises:

extracting a feature vector according to the shopping keyword information and the shopping related information;

inputting the feature vector to a deep neural network model for training and prediction to obtain a prediction result; and

generating the recommendation information according to the prediction result.

In an embodiment, the one or more processors are further configured to:

when no voice information is acquired or no shopping keyword information is extracted, transmit notification information to the server to notify the server to perform interest prediction according to the shopping related information of the user, to generate the recommendation information.

In an embodiment, the shopping keyword information comprises at least one of commodity category information, commodity name information, commodity rating information, and

the shopping related information comprises at least one of basic information of the user, commodity information, shopping history information, and other interactive information.

In a fourth aspect of the present disclosure, there is provided a server, comprising:

one or more processors;

a memory connected to the one or more processors and having one or more programs stored therein, which, when executed by the one or more processors, cause the one or more processors to be configured to:

receive shopping keyword information transmitted by a client, wherein the shopping keyword information is obtained by acquiring voice information of a user and performing voice recognition and keyword extraction on the voice information by the client;

perform interest prediction according to the shopping keyword information and shopping related information of the user to generate recommendation information; and

transmit the recommendation information to the client, so that the client generates shopping recommendation content according to the recommendation information to be displayed by a display.

In an embodiment, the one or more processors are further configured to:

extract a feature vector according to the shopping keyword information and the shopping related information;

input the feature vector to a deep neural network model for training and prediction to obtain a prediction result; and

generate the recommendation information according to the prediction result.

In an embodiment, the one or more processors are further configured to:

receive notification information transmitted by the client, wherein the notification information is transmitted by the client when no voice information is acquired or no shopping keyword information is extracted; and

after receiving the notification information, perform interest prediction according to the shopping related information of the user, to generate the recommendation information.

In an embodiment, the shopping keyword information comprises at least one of commodity category information, commodity name information, and commodity rating information, and

the shopping related information comprises at least one of basic information of the user, commodity information, shopping history information, and other interactive information.

