CN111125523A

CN111125523A - Searching method, searching device, terminal equipment and storage medium

Info

Publication number: CN111125523A
Application number: CN201911328504.5A
Authority: CN
Inventors: 彭璐; 赵安; 于超
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2019-12-20
Filing date: 2019-12-20
Publication date: 2020-05-08
Anticipated expiration: 2039-12-20
Also published as: CN111125523B; WO2021120875A1

Abstract

The application is applicable to the technical field of computers, and provides a search method, a device, terminal equipment and a storage medium based on Artificial Intelligence (AI), wherein the method comprises the following steps: acquiring a search keyword; determining a target search object corresponding to a search keyword according to a pre-established mapping relation list of the search object and the search keyword; and displaying the target search object. The mapping relation list of the search object and the search keyword is established according to the historical search behavior and the operation behavior after the historical search behavior. According to the searching method, the target searching object corresponding to the searching keyword is determined through the mapping relation list of the searching object and the searching keyword, and the more accurate searching object can be recommended for the user.

Description

Searching method, searching device, terminal equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a search method and apparatus based on Artificial Intelligence (AI), a terminal device, and a storage medium.

Background

The existing terminal equipment recommends a search result for a user after the user inputs a search keyword, so that the user can conveniently and quickly search corresponding services. However, the existing search method mainly returns a search result according to the matching degree of the name of the search object and the search keyword input by the user, and for some search objects with names not matched with the search keyword, accurate recommendation cannot be performed, so that the existing search method cannot accurately judge the real search intention of the user.

Disclosure of Invention

The embodiment of the application provides a searching method, a searching device, terminal equipment and a storage medium, so as to accurately judge the real searching intention of a user.

In a first aspect, an embodiment of the present application provides a search method, including:

acquiring a search keyword;

determining a target search object corresponding to a search keyword according to a pre-established mapping relation list of the search object and the search keyword; the mapping relation list of the search object and the search keyword is established according to historical search behaviors and operation behaviors after the historical search behaviors;

and displaying the target search object.

In the embodiment of the application, the mapping relation list of the search object and the search keyword is established according to the historical search behavior and the operation behavior after the historical search behavior, and the operation behavior after the historical search behavior reflects the real search intention of the user.

In a possible implementation manner of the first aspect, the list of mapping relationships between the search object and the search keyword is established in the following manner:

obtaining the searching behavior log information and the operation behavior log information of a user, wherein the searching behavior log information comprises historical searching behaviors, and the operation behavior log information comprises historical operation behaviors. Acquiring a search keyword from the search behavior log information, acquiring an operation behavior within a preset time interval after a user inputs the search keyword from the operation behavior log information, and taking an operation object corresponding to the operation behavior as an initial search object; and calculating a confidence score of the initial search object, wherein the confidence score reflects the association degree of the initial search object and the search keyword, and establishing a mapping relation list of the search object and the search keyword according to the confidence score of the initial search object, so that the initial search object with high association with the search keyword is recommended to the user, and the recommendation accuracy is improved.

In a possible implementation manner of the first aspect, the calculating a confidence score of the initial search object includes:

and acquiring at least one behavior characteristic value of the initial search object from the operation behavior log information, wherein the behavior characteristic value comprises the number of times of use of the initial search object, the average use time of the initial search object and/or the ratio of the number of times of use of the initial search object. And calculating the confidence score of the initial search object according to the at least one behavior characteristic value and a preset weight coefficient corresponding to each behavior characteristic value. The behavior characteristic values represent information of the initial search object operated by the user, the preset weight coefficient reflects the importance degree of each behavior characteristic value, and the confidence score of the initial search object calculated through the behavior characteristic values and the preset weight coefficient corresponding to each behavior characteristic value reflects the intention of the user on the initial search object during searching.

and calculating the confidence score of the initial search object according to the search keyword, the initial search object and a preset prediction model, wherein the preset prediction model is obtained by training a learning model by adopting a machine learning or deep learning algorithm by taking the search keyword, the initial search object corresponding to the search keyword and the confidence score as training samples. The preset prediction model can be used repeatedly, the confidence score of the initial search object is calculated by adopting the preset prediction model, and the stability of the calculation result is ensured.

In a possible implementation manner of the first aspect, the establishing a mapping relationship list between the search object and the search keyword according to the confidence score of the initial search object includes:

calculating the weight value of the search keyword according to the confidence score of the initial search object;

and establishing a mapping relation list of the search object and the search keyword according to the weight value of the search keyword. It can be understood that each search object corresponds to a plurality of search keywords, different search objects may have the same search keyword, and different search keywords may have different weight values for different search objects.

In a possible implementation manner of the first aspect, the calculating a weight value of the search keyword according to the confidence score of the initial search object includes:

converting the confidence score of the initial search object into a confidence score of a search keyword;

and taking the confidence score of the search keyword which is greater than the preset confidence threshold value as the weight value of the search keyword.

Exemplarily, after the weight values of the search keywords are obtained, the search keywords corresponding to each search object are arranged in a descending order according to the weight values of the search keywords, and then a preset number of records are taken, so that a mapping relation list of the search objects and the search keywords can be obtained.

In a possible implementation manner of the first aspect, the determining a target search object corresponding to the search keyword includes:

and taking the search object corresponding to the search keyword with the weight value larger than the preset weight threshold value as the target search object, for example, arranging the search objects in a descending order according to the weight value, and displaying the arranged search objects on the terminal equipment.

In a possible implementation manner of the first aspect, before the obtaining a search keyword from the search behavior log information, the method further includes:

respectively preprocessing the search behavior log information and the operation behavior log information to obtain preprocessed target search behavior log information and target operation behavior log information;

correspondingly, obtaining a search keyword from the search behavior log information, and obtaining an operation behavior within a preset time interval after the search keyword is input by the user from the operation behavior log information, includes:

and acquiring a search keyword from the target search behavior log information, and acquiring the operation behavior within a preset time interval after the search keyword is input by a user from the target operation behavior log information, so as to acquire effective log information and ensure the accuracy of a calculation result.

In a possible implementation manner of the first aspect, after the obtaining, from the operation behavior log information, an operation behavior within a preset time interval after the user inputs the search keyword, the method further includes:

deleting the operation behaviors of which the operation frequency is greater than a first threshold value and the operation behaviors of which the operation frequency is less than a second threshold value from the acquired operation behaviors to obtain effective operation behaviors;

correspondingly, taking the operation object corresponding to the operation behavior as an initial search object, including:

and taking the operation object corresponding to the effective operation behavior as an initial search object, thereby preventing the interference of high-frequency operation and low-frequency operation on the calculation result and ensuring the accuracy of the calculation result.

In a second aspect, an embodiment of the present application provides a search apparatus, including:

the acquisition module is used for acquiring search keywords;

the determining module is used for determining a target search object corresponding to the search keyword according to a mapping relation list of the search object and the search keyword which is established in advance; the mapping relation list of the search object and the search keyword is established according to historical search behaviors and operation behaviors after the historical search behaviors;

and the display module is used for displaying the target search object.

In a possible implementation manner of the second aspect, the search apparatus further includes a mapping relationship establishing module, where the mapping relationship establishing module includes:

an acquisition unit configured to acquire search behavior log information and operation behavior log information of a user;

the extraction unit is used for acquiring search keywords from the search behavior log information, acquiring operation behaviors within a preset time interval after the search keywords are input by a user from the operation behavior log information, and taking operation objects corresponding to the operation behaviors as initial search objects;

a calculation unit for calculating a confidence score of the initial search object;

and the establishing unit is used for establishing a mapping relation list of the search object and the search keyword according to the confidence score of the initial search object.

In a possible implementation manner of the second aspect, the computing unit is specifically configured to:

acquiring at least one behavior characteristic value of the initial search object from the operation behavior log information;

and calculating the confidence score of the initial search object according to the at least one behavior characteristic value and a preset weight coefficient corresponding to each behavior characteristic value.

In one possible implementation manner of the second aspect, the behavior feature value includes a number of times of use of the initial search object, an average usage time of the initial search object, and/or a ratio of the number of times of use of the initial search object.

and calculating the confidence score of the initial search object according to the search keyword, the initial search object and a preset prediction model, wherein the preset prediction model is obtained by training a learning model by adopting a machine learning or deep learning algorithm by taking the search keyword, the initial search object corresponding to the search keyword and the confidence score as training samples.

In a possible implementation manner of the second aspect, the establishing unit is specifically configured to:

and establishing a mapping relation list of the search object and the search keyword according to the weight value of the search keyword.

In a possible implementation manner of the second aspect, the establishing unit is further configured to:

In a possible implementation manner of the second aspect, the determining module is specifically configured to:

and taking the search object corresponding to the search keyword with the weight value larger than a preset weight threshold value as the target search object.

In a possible implementation manner of the second aspect, the mapping relationship establishing module further includes a preprocessing unit, configured to:

correspondingly, the extraction unit is specifically configured to:

and acquiring a search keyword from the target search behavior log information, and acquiring an operation behavior within a preset time interval after the search keyword is input by a user from the target operation behavior log information.

In a possible implementation manner of the second aspect, the mapping relationship establishing module further includes a filtering unit, configured to:

correspondingly, the extraction unit is specifically configured to:

and taking the operation object corresponding to the effective operation behavior as an initial search object.

In a third aspect, an embodiment of the present application provides a terminal device, including: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the search method as described above in the first aspect when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the search method according to the first aspect.

In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the search method of the first aspect.

It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below.

Fig. 1 is a block diagram of a terminal device according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a search method according to a first embodiment of the present application;

fig. 3 is an application scenario diagram of a search method provided in an embodiment of the present application;

fig. 4 is a diagram of another application scenario of the search method provided in the embodiment of the present application;

fig. 5 is a diagram of another application scenario of the search method provided in the embodiment of the present application;

FIG. 6 is a schematic diagram of an operating environment of a search method provided by an embodiment of the present application;

fig. 7 is a flowchart illustrating a method for establishing a mapping relationship list between a search object and a search keyword according to a first embodiment of the present application;

fig. 8 is a flowchart illustrating a method for establishing a mapping relationship list between a search object and a search keyword according to a second embodiment of the present application;

fig. 9 is a schematic structural diagram of a search apparatus according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

The searching method provided by the embodiment of the application can be applied to terminal devices such as a mobile phone, a tablet personal computer, a wearable device, a vehicle-mounted device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a Personal Digital Assistant (PDA) and the like, and can also be applied to intelligent household appliances such as a sound box, a television, a washing machine and the like, and the embodiment of the application does not limit the specific type of the terminal device at all.

Take the terminal device as a mobile phone as an example. Fig. 1 is a block diagram illustrating a partial structure of a mobile phone according to an embodiment of the present disclosure. Referring to fig. 1, the cellular phone includes: a Radio Frequency (RF) circuit 110, a memory 120, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a wireless fidelity (WiFi) module 170, a processor 180, and a power supply 190. Those skilled in the art will appreciate that the handset configuration shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

The following describes each component of the mobile phone in detail with reference to fig. 1:

the RF circuit 110 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives downlink information of a base station and then processes the received downlink information to the processor 180; in addition, the data for designing uplink is transmitted to the base station. Typically, the RF circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc. For example, a user inputs a contact to search in the interface of the call application, the terminal device recommends the contact for the user according to the keyword of the contact input by the user, and the user dials a phone call of the contact to make a call through the RF circuit 110.

The memory 120 may be used to store software programs and modules, and the processor 180 executes various functional applications and data processing of the mobile phone by operating the software programs and modules stored in the memory 120. The memory 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The input unit 130 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the cellular phone. Specifically, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also referred to as a touch screen, may collect touch operations of a user on or near the touch panel 131 (e.g., operations of the user on or near the touch panel 131 using any suitable object or accessory such as a finger or a stylus pen), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 131 may include two parts, i.e., a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch panel 131 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 130 may include other input devices 132 in addition to the touch panel 131. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. For example, a user enters a search keyword through the input interface to perform a search.

The display unit 140 may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit 140 may include a display panel 141, and optionally, the display panel 141 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 131 can cover the display panel 141, and when the touch panel 131 detects a touch operation on or near the touch panel 131, the touch operation is transmitted to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although the touch panel 131 and the display panel 141 are shown as two separate components in fig. 1 to implement the input and output functions of the mobile phone, in some embodiments, the touch panel 131 and the display panel 141 may be integrated to implement the input and output functions of the mobile phone.

The handset may also include at least one sensor 150, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that adjusts the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that turns off the display panel 141 and/or the backlight when the mobile phone is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.

Audio circuitry 160, speaker 161, and microphone 162 may provide an audio interface between the user and the handset. The audio circuit 160 may transmit the electrical signal converted from the received audio data to the speaker 161, and convert the electrical signal into a sound signal for output by the speaker 161; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, which is received by the audio circuit 160 and converted into audio data, which is then processed by the audio data output processor 180 and then transmitted to, for example, another cellular phone via the RF circuit 110, or the audio data is output to the memory 120 for further processing. In the embodiment of the present application, the microphone 162 converts the collected sound signal into an electrical signal, the electrical signal is received by the audio circuit 160 and then converted into audio data, the audio data is output to the processor 180, and the processor 180 performs corresponding search according to the audio data.

WiFi belongs to short-distance wireless transmission technology, and the mobile phone can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 170, and provides wireless broadband Internet access for the user. Although fig. 1 shows the WiFi module 170, it is understood that it does not belong to the essential constitution of the handset, and may be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 180 is a control center of the mobile phone, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the mobile phone and processes data by operating or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby integrally monitoring the mobile phone. Alternatively, processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.

The handset also includes a power supply 190 (e.g., a battery) for powering the various components, and preferably, the power supply may be logically connected to the processor 180 via a power management system, such that functions such as managing charging, discharging, and power consumption are performed via the power management system.

Although not shown, the handset may also include a camera. Optionally, the position of the camera on the mobile phone may be front-located or rear-located, which is not limited in this embodiment of the present application. For example, the mobile phone scans the two-dimensional code through the camera, and performs corresponding search according to the scanned two-dimensional code.

Optionally, the mobile phone may include a single camera, a dual camera, or a triple camera, which is not limited in this embodiment.

For example, a cell phone may include three cameras, one being a main camera, one being a wide camera, and one being a tele camera.

Optionally, when the mobile phone includes a plurality of cameras, all the cameras may be arranged in front of the mobile phone, or all the cameras may be arranged in back of the mobile phone, or a part of the cameras may be arranged in front of the mobile phone, and another part of the cameras may be arranged in back of the mobile phone, which is not limited in this embodiment of the present application.

In addition, although not shown, the mobile phone may further include a bluetooth module, etc., which will not be described herein.

The following describes a search method provided in an embodiment of the present application with reference to fig. 1. As shown in fig. 2, the search method provided in the embodiment of the present application includes:

s101: and acquiring a search keyword.

In an application scenario, when a user needs to search, voice, a character string or a two-dimensional code is input on a search interface of a terminal device, and the terminal device extracts a search keyword from user input information.

In another application scenario, when the terminal device needs to recommend information for the user, such as recommending videos, products, services, and the like, the user preference may be obtained according to the historical operation behavior of the user, such as a shopping record, a video watching record, and the like, and the search keyword may be obtained from the user preference.

Illustratively, as shown in fig. 3, in an application scenario, a user inputs "i want to take a taxi" in an application search interface to perform application search, and the extracted search keyword is "take a taxi". In another application scenario, as shown in fig. 4, the user inputs voice "pay" through a voice assistant on the terminal device, the terminal device recognizes "pay" from the input voice and displays the "pay" in the search bar of the application, and the corresponding search keyword is "pay". In yet another application scenario, as shown in fig. 5, the user enters "i want to see strong head" in the video search interface, and the search keyword extracted by the video application is "strong head".

S102: determining a target search object corresponding to a search keyword according to a pre-established mapping relation list of the search object and the search keyword; the mapping relation list of the search object and the search keyword is established according to historical search behaviors and operation behaviors after the historical search behaviors.

Wherein the search object may be an application, music, video, news, web page, etc. The mapping relation list of the search object and the search keyword can be established by counting the historical search behavior of the current user and the operation behavior after the historical search behavior by the terminal equipment; or the mapping relation list is sent to the terminal device after the server counts the historical searching behaviors of all users and the operation behaviors after the historical searching behaviors are established; or the historical search behavior and the operation behavior after the historical search behavior are collected by the server, the historical search behavior and the operation behavior are sent to the terminal equipment, and the historical search behavior and the operation behavior are counted by the terminal equipment.

For example, as shown in fig. 6, the terminal device 1 is communicatively connected to a server 2, where the server 2 may be a server, a server cluster composed of several servers, or a cloud computing service center. The server counts the historical searching behaviors and the operation behaviors of all the terminal devices in communication connection with the server, and establishes a mapping relation list of the searching objects and the searching keywords. When acquiring the search keyword, the terminal device 1 acquires a mapping relationship list between the search object and the search keyword, which is sent by the server. In the mapping relationship list of the search objects and the search keywords, one search keyword may correspond to a plurality of search objects, and the terminal device may use all of the plurality of search objects corresponding to the search keyword as the target search object, or may use a part of the search objects or one search object corresponding to the search keyword as the target search object.

S103: and displaying the target search object.

Illustratively, as shown in fig. 3, in the mapping relationship list of the search object and the search keyword, the target search object corresponding to "taxi" is "drip taxi", "fast dog taxi", "high-grade map" and "special state taxi", and the terminal device displays the above application program list according to the mapping relationship list of the search object and the search keyword. As shown in fig. 4, in the mapping relationship list of the search object and the search keyword, the target search object corresponding to "pay" is "pay treasure", "wallet", "WeChat" and "cloud flash payment", and the terminal device displays the application list according to the mapping relationship list of the search object and the search keyword. As shown in fig. 5, in the mapping relationship list between the search object and the search keyword, the target search object corresponding to "strong head" is "expedition diary 2 in which bears are present", "(" expedition diary in which bears are present "," "complete collection of bears are present", and "(" fantasy space in which bears are present "), and the terminal device displays the video list according to the mapping relationship list between the search object and the search keyword.

In the above embodiment, since the list of the mapping relationship between the search object and the search keyword is established according to the historical search behavior and the operation behavior after the historical search behavior, the search object may reflect the real intention of the user after the search keyword is input. When the user inputs the search keyword, the target search object corresponding to the search keyword is determined according to the mapping relation list of the search object and the search keyword, and a more accurate search object can be recommended for the user.

In the search method provided in the embodiment of the present application, a method for establishing a mapping relationship list between a search object and a search keyword is described in detail below.

As shown in fig. 7, a method for establishing a mapping relationship list between a search object and a search keyword provided in a first embodiment of the present application includes:

s201: and acquiring the search behavior log information and the operation behavior log information of the user.

Specifically, stored in the search behavior log information is the historical search behavior of the user, and the search behavior log information includes any one or more items of a user ID, a search time, an original search string, a search exposure list, a search click list, and the like. Wherein the search exposure list stores: the terminal device recommends a search object for the user according to the extracted search keyword, for example, a list of applications recommended for the user according to the search keyword. The search click list stores: and when the user does not select the search object from the search exposure list, the record of the corresponding search click list is empty.

The operation behavior log information stores historical operation behaviors of the user, and comprises one or more items of user ID, operation behavior occurrence time, name of a search object corresponding to the operation behavior, category of the search object, label of the search object and the like. Wherein the category of the search object and the tag of the search object are generated according to the feature of the search object. For example, when the search object is the application "WeChat", the category of the search object is chat, and the tags of the search object are social contact, communication, scanning, voice, and the like.

In one possible implementation, in order to improve the accuracy of the mapping relationship list, the search behavior log information and the operation behavior log information of all the terminal devices connected to the server may be acquired.

For example, for a scenario in which a user searches for an application, a user inputs an original search string "i want to take a car" on an application search interface of a terminal device, the recommended applications on the terminal device are a fast dog car driver edition, a fast dog car, and a fast dog car (fast application), the recommended applications on the terminal device form a search exposure list, the user does not click the applications from the exposure list, and the corresponding click list is empty. The time to start the search, the search for the corresponding user ID, the search for the exposure list, the search for the click list are recorded to get the first record of table 1. In table 1, each search record of each user ID is recorded in turn, resulting in the search behavior log information of the application program shown in table 1.

TABLE 1

Correspondingly, the time of using the application program, the name of the application program, the type of the application program, the tag of the application program, and the like are recorded for each terminal device, and the behavior log information of the used application program shown in table 2 is obtained.

TABLE 2

S202: and preprocessing the search behavior log information and the operation behavior log information to obtain target search behavior log information and target operation behavior log information.

Specifically, data cleaning or feature engineering processing is performed on the acquired search behavior log information and the acquired operation behavior log information, including removing error data, repeated data, abnormal data and the like, so as to obtain target search behavior log information and target operation behavior log information. For example, data records with incomplete fields, records with conflicting time records, and records with missing fields are removed.

S203: extracting a search keyword from the target search behavior log information, acquiring an operation behavior within a preset time interval after the search keyword is input by a user from the target operation behavior log information, and taking an operation object corresponding to the operation behavior as an initial search object.

Specifically, the search time is extracted from the target search behavior log information, and a search keyword is extracted from an original search string input by the user. For example, the keyword extracted from "i want to take a car" is "taking a car", "help me to order" is "order", "what news is there today" is "news", and the like. And meanwhile, extracting the time of the occurrence of the operation behavior, the duration of the operation behavior and the name of the operation object from the target operation behavior log information. For example, for the behavior of the search application, the name of the application opened by the user and the time when the application starts to be used are extracted from the target operation behavior log information. According to the search time and the occurrence time of the operation behaviors, counting the operation objects corresponding to the operation behaviors of the user after inputting the original search character string each time, and taking the operation objects corresponding to all the operation behaviors in a preset time interval after inputting the original search character string as initial search objects.

Illustratively, for the operation behavior of using the application program, the application program used in a preset time interval after the user inputs the original search string is counted, and the counted application program is taken as the application program corresponding to the search keyword. For example, setting a time interval to be 30 seconds, setting an original search string input by a certain user as "i want to taxi", setting a search time to be "2019062015: 00: 00", and setting an extracted search keyword as "taxi", counting application programs used in a time period from "2019062015: 00:00 to 2019062015:00: 30" on the terminal device, wherein the application programs in the time period are initial search objects.

In a possible implementation manner, in order to improve the accuracy of the calculation result, the initial search objects corresponding to each search keyword on all the terminal devices connected to the server are counted, and the same initial search objects corresponding to the same search keywords of all the users are combined, so that each search keyword corresponds to a plurality of initial search objects. For example, for an operation behavior using an application, if a search keyword extracted from an original search string of a user is "car-typing", the application used by the user within 30 seconds after the search is recorded; and when the search keyword of all users is 'taxi taking', counting the application programs used within 30 seconds after searching, and combining the same application programs to obtain the statistical results shown in the table 3.

TABLE 3

Due to some common high-frequency operation behaviors, the operation behaviors can be frequently found in a search list, and the result is interfered; there are also some low frequency operation behaviors that use less frequency, and the calculation error is larger because the amount of data is too small. In a possible implementation manner, after the operation behaviors within a preset time interval after the search keyword is input by the user are acquired from the target operation behavior log information, the high-frequency operation behavior with the operation frequency greater than the first threshold and the low-frequency operation behavior with the operation frequency less than the second threshold are deleted, so that the effective operation behaviors are obtained, and the operation objects corresponding to the effective operation behaviors are used as initial search objects, so that the calculation accuracy is improved. Illustratively, in the statistics of the applications corresponding to the search keywords, the high-frequency applications with high use frequency and the low-frequency applications with low use frequency are removed. For example, the first 5 applications arranged corresponding to "taxi taking" in table 3 are "WeChat", "drip trip", "Paibao", "God map" and "today's headline", respectively, where the "WeChat", "Paibao" and "today's headline" belong to high-frequency applications, and when a high-frequency operation behavior with a high operation frequency needs to be removed, the three applications are removed from the application list; when it is necessary to remove the low-frequency operation behavior with a low operation frequency, for example, the application program with the usage number < ═ 1000 times can be removed, that is, the application program with the low usage frequency is removed from the list.

It will be appreciated that in another possible implementation, only one initial search object corresponding to each search keyword may be counted. For example, for an application search with a search keyword of "taxi taking", only the application that is used first after the user inputs the original search string, or the application that is used the most times after the user inputs the original search string, or the application that is used the longest time after the user inputs the original search string may be counted.

S204: and acquiring at least one behavior characteristic value of the initial search object.

In a possible implementation manner, the behavior feature value is obtained by counting initial search objects corresponding to the search keywords of all the users. The behavior feature value includes the number of uses of the initial search object, the average usage time of the initial search object, whether the initial search object is selected when displayed in the search exposure list, and/or the ratio of the number of uses of the initial search object. For example, for the initial search object "height map", the behavior feature value includes the number of times of using "height map", the average time length of using "height map", whether or not "height map" exists in the recommendation list when the search keyword is "taxi-taking", whether or not the user clicks the usage number ratio of "height map", "height map" from the recommendation list, and the like.

Exemplarily, after initial search objects corresponding to search keywords of all users are combined, for a certain initial search object, an average usage duration is calculated according to operation durations of all users on the initial search object; counting the times of operating the initial search object by all users; and calculating the proportion of the number of people operating the initial search object in the total number of people to obtain the use frequency ratio of the initial search object, and combining all records to obtain a user behavior broad table. For example, as shown in table 4, in the search of the application, the search keyword is "taxi taking", the applications used by all users within 30 seconds after the search when the search keyword is "taxi taking" are counted, and the "average usage duration", "usage number", and "usage number ratio" of the corresponding applications are calculated, so as to obtain the application width table corresponding to the search keyword "taxi taking".

TABLE 4

S205: and calculating the confidence score of the initial search object according to the at least one behavior characteristic value and a preset weight coefficient corresponding to each behavior characteristic value.

Specifically, the product of each behavior feature value and the corresponding preset weight coefficient is summed to obtain the confidence score of the corresponding initial search object.

For example, assuming that the behavior feature value for calculating the confidence score of the initial search object includes the number of times of use of the initial search object, the average usage duration of the initial search object, and the usage count ratio of the initial search object, the weight coefficients of the number of times of use of the initial search object, the average usage duration of the initial search object, and the usage count ratio of the initial search object of one initial search object corresponding to a certain search keyword are 0.4, 0.3, and 0.3, respectively, after each behavior feature value is normalized, the number of times of use of the initial search object, the average usage duration of the initial search object, and the usage count ratio of the initial search object are 0.5, and 0.1, respectively, and the product of the value corresponding to each behavior feature value and the weight coefficient is added, that is, 0.4 × 0.5+0.3 × 0.1 ═ 0.38, to obtain the confidence score.

For another example, in an application scenario, if the behavior feature value for calculating the confidence score includes the ratio of the number of times of use of the initial search object, the confidence score is calculated based on the ratio of the number of times of use of the initial search object, for example, in table 5, in statistics of usage records of the application program, if 53% of people who have searched for "driving" have used "drip-out row", the ratio of the number of times of use of "drip-out row" corresponding to "driving" is 0.53, and the confidence of "drip-out row" corresponding to "driving" is 0.53. Similarly, if the percentage of usage times of the "high-level map" corresponding to the "taxi taking" is calculated to be 0.25, the confidence level of the "high-level map" corresponding to the "taxi taking" is calculated to be 0.25, and the confidence level score of each application program corresponding to each search keyword is obtained.

TABLE 5

S206: and establishing a mapping relation list of the search object and the search keywords according to the confidence score of the initial search object.

In one possible implementation, the confidence score of the initial search object is converted into a confidence score of the search keyword, and the confidence score of the search keyword greater than a preset confidence threshold is used as a weight value of the search keyword. For example, if the confidence score of the initial search object "pay for treasure" corresponding to the search keyword "pay" is 0.35, the confidence score of "pay" in the search keyword corresponding to "pay for treasure" is 0.35, and in addition, the search keyword corresponding to "pay for treasure" includes "order", "take out", and the like. And sequentially converting the confidence scores of the initial search objects corresponding to the search keywords into the confidence scores of the search keywords corresponding to the initial search objects. And for each initial search object, performing descending order on the search keywords according to the corresponding confidence score, taking the confidence score in the record of which the confidence score is greater than a preset confidence threshold value as a weight value, wherein each weight value corresponds to one record, and obtaining a mapping relation list of the search objects and the search keywords. In the mapping relation list of the search objects and the search keywords, each initial search object corresponds to a plurality of search keywords, and each search keyword corresponds to a weight value. For example, for the search of the application, the confidence scores of the applications in table 5 are converted into confidence scores of the search keywords, and after the confidence scores are arranged in a descending order, the confidence scores in the records in the top 5 are the weight values of the search keywords, where the plurality of search keywords and the corresponding weight values corresponding to the "drip travel" are ("drip", 0.9), ("travel", 0.56), ("taxi", 0.53), ("drive by generation", 0.45), ("take out", 0.25), and the search keywords are the intention tags. And counting the weight value of each search keyword corresponding to each application program to obtain a mapping relation list of the application programs and the search keywords shown in table 6.

TABLE 6

When a user searches next time, extracting a search keyword from an original search character string, taking a search object corresponding to a record with the weight value of the search keyword being greater than a preset weight threshold value as a target search object according to a mapping relation list of the search object and the search keyword, and recommending the target search object to the user. For example, when the user inputs a "car-hitting" search application, the applications corresponding to the "car-hitting" are ranked according to the weight value, and the applications corresponding to the top 5 sorted records are recommended to the user.

In the above embodiment, by obtaining the search behavior log information and the operation behavior log information of the user, the operation behavior within the preset time interval after the search keyword is input is extracted from the log information, and the operation object corresponding to the operation behavior is used as the initial search object, and the confidence score of the initial search object is calculated according to at least one behavior characteristic value of the initial search object and the preset weight coefficient corresponding to each behavior characteristic value. Since the behavior feature value represents feature information of the initial search object, a confidence score calculated from the behavior feature value represents an intention of the user to operate the initial search object. According to the confidence score of the initial search object, a mapping relation list of the search object and the search keyword is established, the real search intention of the user can be accurately reflected, and when the user searches, more accurate service can be recommended for the user according to the mapping relation list of the search object and the search keyword.

As shown in fig. 8, a search method provided in the second embodiment of the present application includes:

s301: and acquiring the search behavior log information and the operation behavior log information of the user.

S302: and preprocessing the search behavior log information and the operation behavior log information to obtain target search behavior log information and target operation behavior log information.

S303: extracting a search keyword from the target search behavior log information, acquiring an operation behavior within a preset time interval after the search keyword is input by a user from the target operation behavior log information, and taking an operation object corresponding to the operation behavior as an initial search object.

Wherein, S301-S303 are the same as S201-S203, and are not described herein again.

S304: and calculating the confidence score of the initial search object according to the search keyword, the initial search object and a preset prediction model.

The preset prediction model is obtained by training a learning model by using a machine learning or deep learning algorithm and taking a search keyword, an initial search object corresponding to the search keyword and a confidence score as training samples. Specifically, the search keyword and an initial search object corresponding to the search keyword are input into the learning model, the characteristics of the search keyword, the characteristics of the initial search object and the characteristics of the operation behavior corresponding to the search keyword are extracted, a corresponding confidence score is output, the parameters of the learning model are optimized according to the difference between the output confidence score and the confidence score in the training sample, the optimal parameters of the learning model are obtained, and a preset prediction model is generated according to the optimal parameters. The search keyword features are any one or more of the heat degree of the keyword, the total search times, the search time ratio, word2vec word vectors corresponding to the search keyword, features of similar keywords of the search keyword, and the like. The characteristics of the initial search object comprise any one or more of the name, category, label of the search object, corresponding word2vec word vector, characteristics of similar search objects of the search object, and the like. The operation behavior is characterized by any one or more of average use time, use heat, total use times, use time ratio and the like of the initial search object after searching.

In a possible implementation manner, the learning model may be a machine learning model such as logistic regression, gradient spanning tree, random forest, or a deep learning model such as a Convolutional Neural Network (CNN) model, a Fully Connected Neural Network (FCNN) model, and the method for training the learning model may be a supervised learning algorithm or a semi-supervised learning algorithm. Illustratively, a learning model is trained by adopting a semi-supervised classification algorithm, and for a first preset number of initial search objects, a confidence score of each initial search object corresponding to each search keyword is set according to each search keyword corresponding to the initial search objects. For example, the number of the applications for which the confidence score is to be calculated is 1000, and 100 applications are selected from the applications, wherein the confidence score of the "drip travel" corresponding to "taxi taking" is set to 0.5, the confidence score of the "height map" corresponding to "taxi taking" is set to 0.3, the confidence score of the "pay treasure" corresponding to "pay" is set to 0.4, and the confidence score of the "WeChat" corresponding to "pay" is set to 0.4, and the confidence scores of the 100 applications and the corresponding search keywords are sequentially set by this method. And taking the set first preset number of initial search objects, the set search keywords and the set confidence score as training samples, and training and learning the learning model to obtain a first candidate model. And generating corresponding training samples according to a second preset number of initial search objects, and performing 'relearning' on the first candidate model to optimize parameters of the first candidate model and correct inaccurate confidence score. For example, after selecting 100 applications from 1000 applications to be subjected to confidence score calculation to train a first candidate model, inputting the remaining 900 applications into the first candidate model, selecting 100 applications with the highest confidence level according to the confidence score corresponding to each application, and generating a training sample again according to the 100 applications with the highest confidence level and the 100 applications used for training the first candidate model, so as to perform "relearning" on the first candidate model to optimize parameters of the first candidate model, thereby obtaining a second candidate model. The method is adopted to carry out iteration calculation in sequence to obtain a prediction model. The selection method of the application program with the highest reliability may be selected according to the difference between the output results of the plurality of learning models. For example, the number of the first candidate models is 3, the remaining 900 application programs are input into the 3 first candidate models, and the application program corresponding to the confidence score with the minimum difference between the output results of the 3 first candidate models is selected as the application program with the highest confidence level. The confidence score of the initial search object can be calculated by inputting the search keyword and the initial search object into the prediction model.

S305: and establishing a mapping relation list of the search object and the search keywords according to the confidence score of the initial search object.

S305 is the same as S206, and is not described herein again.

In the embodiment, by obtaining the search behavior log information and the operation behavior log information of the user, the operation behavior within the preset time interval after the search keyword is input is extracted from the log information, the operation object corresponding to the operation behavior is used as the initial search object, the confidence score of the initial search object is calculated according to the search keyword, the initial search object and the preset prediction model, the preset prediction model is generated by counting and repeatedly training a large amount of data, the calculation result is stable and has universality, the mapping relation list of the search object and the search keyword is established according to the confidence score of the initial search object, the real search intention of the user can be accurately reflected, and when the user searches, more accurate service can be recommended for the user according to the mapping relation list of the search object and the search keyword.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Fig. 9 shows a block diagram of a searching apparatus provided in the embodiment of the present application, corresponding to the searching method described in the above embodiment, and only shows a part related to the embodiment of the present application for convenience of description.

Referring to fig. 9, the search apparatus includes:

an obtaining module 10, configured to obtain a search keyword;

the determining module 20 is configured to determine a target search object corresponding to a search keyword according to a mapping relationship list between the search object and the search keyword, where the mapping relationship list is established in advance; the mapping relation list of the search object and the search keyword is established according to historical search behaviors and operation behaviors after the historical search behaviors;

and a display module 30, configured to display the target search object.

In a possible implementation manner, the search apparatus further includes a mapping relationship establishing module, where the mapping relationship establishing module includes:

In a possible implementation manner, the computing unit is specifically configured to:

In one possible implementation, the behavior feature value includes a number of times of use of the initial search object, an average usage time of the initial search object, and/or a ratio of the number of times of use of the initial search object.

In a possible implementation manner, the establishing unit is specifically configured to:

In a possible implementation manner, the establishing unit is further configured to:

In a possible implementation manner, the determining module is specifically configured to:

In a possible implementation manner, the mapping relationship establishing module further includes a preprocessing unit, configured to:

correspondingly, the extraction unit is specifically configured to:

In a possible implementation manner, the mapping relationship establishing module further includes a filtering unit, configured to:

correspondingly, the extraction unit is specifically configured to:

It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A method of searching, comprising:

acquiring a search keyword;

and displaying the target search object.

2. The search method of claim 1, wherein the list of mapping relationships between the search object and the search keyword is established by:

acquiring search behavior log information and operation behavior log information of a user;

acquiring a search keyword from the search behavior log information, acquiring an operation behavior within a preset time interval after a user inputs the search keyword from the operation behavior log information, and taking an operation object corresponding to the operation behavior as an initial search object;

calculating a confidence score for the initial search object;

and establishing a mapping relation list of the search object and the search keywords according to the confidence score of the initial search object.

3. The search method of claim 2, wherein said calculating a confidence score for said initial search object comprises:

4. The search method of claim 3, wherein the behavior feature value includes a number of uses of the initial search object, an average usage time period of the initial search object, and/or a ratio of the number of uses of the initial search object.

5. The search method of claim 2, wherein said calculating a confidence score for said initial search object comprises:

6. The method of claim 2, wherein said building a list of mapping relationships between said search object and search keywords based on said initial search object's confidence score comprises:

7. The method of claim 6, wherein said calculating a weight value for said search keyword based on a confidence score of said initial search object comprises:

8. The searching method of claim 7, wherein the determining the target search object corresponding to the search keyword comprises:

9. The search method according to claim 2, further comprising, before said obtaining a search keyword from the search behavior log information:

10. The search method according to any one of claims 2 to 9, further comprising, after acquiring, from the operation behavior log information, an operation behavior within a preset time interval after the search keyword is input by the user, the step of:

11. A search apparatus, comprising:

the acquisition module is used for acquiring search keywords;

and the display module is used for displaying the target search object.

12. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 10 when executing the computer program.

13. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 10.