CN115203544A - Recommendation method and device, electronic device and medium - Google Patents

Abstract

The disclosure provides a recommendation method and device, electronic equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent search. The implementation scheme is as follows: receiving a plurality of characters input into a search box; determining recognition results of at least two different languages corresponding to a group of continuous characters in the plurality of characters; and determining recommended search terms for the plurality of characters based on the recognition results of the at least two different languages.

Description

Recommendation method and device, electronic device and medium

Technical Field

The present disclosure relates to the field of artificial intelligence technologies, and in particular, to the field of intelligent search technologies, and in particular, to a recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

Background

Artificial intelligence is the subject of research that causes computers to simulate certain human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge map technology and the like.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been acknowledged in any prior art, unless otherwise indicated.

Disclosure of Invention

The present disclosure provides a recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

According to an aspect of the present disclosure, there is provided a recommendation method including: receiving a plurality of characters input into a search box; determining recognition results of at least two different languages corresponding to a group of continuous characters in the plurality of characters; and determining recommended search terms for the plurality of characters based on the recognition results of the at least two different languages.

According to another aspect of the present disclosure, there is provided a recommendation device including: a receiving unit configured to receive a plurality of characters input in a search box; a first determination unit configured to determine, for a set of consecutive characters of a plurality of characters, recognition results of at least two different languages corresponding to the consecutive characters; and a second determination unit configured to determine recommended search terms for the plurality of characters based on recognition results of at least two different languages.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above-described method.

According to another aspect of the disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the above-described method when executed by a processor.

According to one or more embodiments of the present disclosure, the accuracy of search recommendation can be improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a recommendation method according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of a recommendation method according to an embodiment of the present disclosure;

4A-4C illustrate application scenario diagrams of a recommendation method according to embodiments of the present disclosure;

FIG. 5 shows a block diagram of a recommender according to an embodiment of the present disclosure; and

FIG. 6 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

When a user performs a search, the system typically provides search results based on the user entering a number of characters in a search box. In order to improve the search efficiency and accuracy of the user, in the related art, the search application generally provides a search recommendation function based on a plurality of characters input by the user. For example, after the user enters "spring festival" in the search box, a plurality of recommended search terms such as "spring festival leaping evening", "spring festival custom", "spring festival vacation" may be displayed below the search box for selection by the user.

However, the determination of the recommended search term is dependent on the characters entered by the user, which requires that the user must correctly enter the characters for the search to be able to obtain an accurate recommended search term. Conversely, if the user enters a wrong character, the wrong recommended search term is obtained based on the wrong character. As such, effective search recommendations may not be provided to the user, resulting in a poor user experience.

Based on this, the present disclosure proposes a recommendation method that determines, for a group of consecutive characters among a plurality of characters input in a search box, recognition results of at least two different languages corresponding to the consecutive characters, and determines a recommended search term based on the recognition results of the at least two different languages. Therefore, even if the user inputs the wrong language under the conditions that the input method language is not switched timely and the like, the search recommendation can be executed based on the multi-language identification result of the continuous characters in the characters, the recommendation deviation caused by the wrong language input by the user is avoided, the search recommendation accuracy is improved, and the user experience is improved.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an example system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable any one of the steps of the recommendation method to be performed.

In some embodiments, the server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of client devices 101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a client device 101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein, and is not intended to be limiting.

The user may use the client device 101, 102, 103, 104, 105, and/or 106 to receive a plurality of characters input by the user and to feedback to the user that the search term is recommended. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices 101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablets, personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a blockchain network, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the client devices 101, 102, 103, 104, 105, and/or 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and/or 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the conventional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

Fig. 2 shows a flowchart of a recommendation method according to an exemplary embodiment of the present disclosure, the method 200 comprising: step S201, receiving a plurality of characters input into a search box; step S202, aiming at a group of continuous characters in a plurality of characters, determining recognition results of at least two different languages corresponding to the continuous characters; and step S203, determining recommended search terms aiming at a plurality of characters based on the recognition results of at least two different languages.

In the searching process, if the input French language is not switched timely, and the like, a plurality of characters input into the search box by a user are easy to be wrong. For example, the user intends to input the english word "ant", but the input method is erroneously set to the chinese input mode. In this case, after the user inputs the alphabetical characters "a", "n", and "t", and presses a confirmation key (such as a space, etc.), an erroneous chinese character "press day" may be input in the search box.

Based on the recommendation method provided by the present disclosure, multi-language recognition can be simultaneously performed for a group of consecutive characters among a plurality of characters input by a user. For example, for the continuous character "by day", the chinese recognition result "by day" corresponding to "by day" and the english recognition result "ant" corresponding to "by day" are determined. On the basis of the multi-language identification result, the recommended search item is determined, so that the recommendation deviation caused by wrong language input of the user can be made up, the accuracy of search recommendation is improved, and the user experience is improved.

In step S201, the characters input into the search box may be character strings of a single language or character strings mixed with multiple languages, which is not limited herein.

In step S202, a set of consecutive characters may be a string of a single language in a plurality of characters. In other words, in the case where a plurality of characters are character strings in which a plurality of languages are mixed, it is possible to divide the plurality of characters into a plurality of character strings of a single language based on a change in language and perform a plurality of languages recognition for each character string, respectively.

According to some embodiments, the consecutive characters may be consecutive chinese characters, and the consecutive characters are determined based on a plurality of letters input, and wherein determining recognition results of at least two different languages corresponding to the consecutive characters may include: determining the continuous characters as Chinese recognition results corresponding to the continuous characters; and determining a plurality of letters for determining the continuous characters as English recognition results corresponding to the continuous characters.

Therefore, the Chinese and English recognition results corresponding to the Chinese character string can be efficiently and accurately recognized, and a multilingual recommendation basis is provided for subsequent determination of search recommendation items.

For example, the consecutive chinese characters are "by day", and "by day" is determined based on the alphabetic characters "a", "n", and "t" input by the user. In this case, it may be determined that the chinese recognition result corresponding to "by day" is "by day" and the english recognition result corresponding to "by day" is "ant". Both the Chinese recognition result "by day" and the English recognition result "ant" can be used as a recommendation basis for subsequently determining recommended search terms.

In one embodiment, the plurality of letters used to determine the successive Chinese characters may be obtained by an input method application used by the user.

In another embodiment, alphabetic characters typed by a user may be cached by a search application during the course of the user inputting successive chinese characters; and when the user determines to input the Chinese character in the search box, such as inputting the Chinese character by clicking a space key, taking the cached letter characters as a plurality of letters corresponding to the Chinese character.

According to some embodiments, the consecutive characters may be consecutive alphabetic characters, and wherein determining the recognition results of the at least two different languages corresponding to the consecutive characters may include: determining the continuous characters as English recognition results corresponding to the continuous characters; and determining a Chinese recognition result corresponding to the continuous characters based on the Pinyin represented by each of the continuous characters.

Therefore, the Chinese and English recognition results corresponding to the English character string can be efficiently and accurately recognized, and a multilingual recommendation basis is provided for subsequent determination of search recommendation items.

In one embodiment, determining the chinese recognition results corresponding to the consecutive characters based on the pinyin represented by each of the consecutive characters may include: for each of the continuous alphabetical characters, one of the Chinese characters in the Chinese recognition result is determined based on the initial consonant corresponding to the character in response to the pinyin represented by the character being the initial consonant.

In particular, in the case where the pinyin represented by a character subsequent to the initial character is not a final, a chinese character is determined based on the initial character itself; under the condition that pinyin represented by a plurality of continuous characters after the initial consonant character is vowel, determining a Chinese character based on the initial consonant character and the plurality of continuous vowel characters.

For example, the consecutive alphabetic character is "jtataq". And determining a Chinese recognition result corresponding to the jtataq based on the Pinyin represented by each jtataq. Specifically, by distinguishing the initial consonant and the final sound in the jtiatq, the jtiatq is divided into four parts, namely j, tia, t and q, each part corresponds to one Chinese character, and then the Chinese recognition result corresponding to the jtiatq is determined as the weather of the four Chinese characters.

It will be appreciated that based on the pinyin represented by each of the consecutive characters, it may be possible to determine a variety of chinese recognition results. In this case, each determined Chinese recognition result can be applied to the subsequent process of determining the recommended search term; one Chinese recognition result with the highest semantic consistency or the highest use frequency in the plurality of Chinese recognition results can also be applied to the subsequent process of determining the recommended search term.

After determining the recognition results of at least two different languages, step S203 may be further performed to determine recommended search terms to be fed back to the user.

According to some embodiments, determining the recommended search term for the plurality of characters based on the recognition results in the at least two different languages may include: aiming at each recognition result of at least two different languages, determining a plurality of candidate search terms corresponding to the recognition result; ranking the plurality of candidate search terms corresponding to each of the at least two recognition results to determine a ranked result; and determining a recommended search term for the plurality of characters based on the ranking results.

The step of sorting the candidate search terms corresponding to each of the at least two recognition results is to perform uniform sorting on the candidate search terms corresponding to each recognition result, in other words, the determined sorting result simultaneously includes the candidate search terms of a plurality of different recognition results.

Therefore, the recommended search terms under the recognition results of the different languages can be provided for the user based on the comprehensive sequencing of the candidate search terms corresponding to the recognition results of the different languages.

According to some embodiments, for each of the recognition results of at least two different languages, determining the candidate search terms corresponding to the recognition result may include: determining at least one recommendation expression corresponding to the recognition result by performing semantic analysis on the recognition result; and generating a plurality of candidate search terms corresponding to the recognition result based on at least one recommendation expression, wherein each candidate search term comprises one of the at least one recommendation expression. Therefore, the candidate search term corresponding to the recognition result of each language can be effectively constructed according to the recognition result of each language.

The recommendation expression corresponding to the recognition result may be the recognition result itself, or may be an expression obtained by correcting, supplementing, or reasoning the recognition result. Preferably, the recommended expression may be an expression having a higher search frequency in association with the recognition result.

It can be understood that, since the plurality of characters input into the search box may include a plurality of groups of consecutive characters at the same time, in addition to the recommended expression of the recognition result, the plurality of candidate search terms corresponding to the recognition result may also include recommended expressions corresponding to other consecutive characters at the same time.

According to some embodiments, ranking the plurality of candidate search terms for each of the at least two recognition results may include: ranking is performed based on the degree of association between each candidate search term and the user representation.

By combining the user portrait to execute sorting, a personalized sorting result aiming at the current user can be generated, so that the sorting result can be more suitable for the search expectation of the current user, the recommendation accuracy is improved, and the user experience is improved.

For example, if the current user is determined to be a "designer" based on the user representation, candidate search terms related to art and design, i.e., candidate search terms associated with the "designer" to a higher degree, may be set to a higher weight value, so that the candidate search terms get a higher rank.

And finally, determining the candidate search terms with the top ranking in the ranking result and the preset number as recommended search terms. The determined recommended search terms may be displayed on an interface of the search application for selection by the user.

Fig. 3 shows a schematic diagram of a recommendation method according to an exemplary embodiment of the present disclosure. As shown in fig. 3, the recommendation method may be implemented through interaction between the mobile terminal 310 and the server 320.

The input module 311 in the mobile terminal 310 receives a plurality of characters input by the user in the search box and transmits the plurality of characters to the server 320.

The server 320 receives the plurality of characters. For a group of consecutive characters of the plurality of characters, a chinese recognition result is determined by the chinese recognition module 321, and an english recognition result is determined by the english recognition module 322. The Chinese recognition result is transmitted to the Chinese recommending module 323 through the Chinese recognizing module 321 to determine a plurality of candidate search terms corresponding to the Chinese recognition result. The english recognition result is transmitted to the english recommendation module 324 through the english recognition module 322 to determine a plurality of candidate search terms corresponding to the english recognition result.

The candidate search terms corresponding to the chinese recognition result and the candidate search terms corresponding to the english recognition result are input to the ranking module 325, and the candidate search term with the highest ranking and the preset number is determined as the recommended search term based on the ranking result of the ranking module 325. The server 320 transmits the determined preset number of recommended search terms to the mobile terminal 310.

The mobile terminal 310 receives a preset number of recommended search terms and displays the preset number of recommended search terms in the recommendation display module 312 according to a sequence.

Therefore, the recommended search item can be determined on the basis of the multi-language identification result, the recommendation deviation caused by the wrong language input of the user is made up, the accuracy of search recommendation is improved, and the user experience is improved.

It is to be understood that, in the recommendation method shown in fig. 3, the two recognition manners of chinese and english are only used for convenience of description, and the present disclosure does not limit the number of the recognition results of different languages determined.

Fig. 4A to 4C are application scene diagrams illustrating a recommendation method according to an exemplary embodiment of the present disclosure.

In fig. 4A, although the user intends to input the english character "ant", the input method gives a plurality of chinese expressions corresponding to "ant" because the user is currently in the chinese input mode, and after the user presses the space bar, the chinese expression "press day" is input into the search box.

In fig. 4B, the user inputs "design by day" in the search box, performs chinese recognition on "by day" for the consecutive characters "by day" to obtain "by day", and performs english recognition on "by day" to obtain "ant". Based on the recognition results of the two languages "by day" and "ant", a plurality of recommended search terms are obtained as shown in fig. 4B.

In fig. 4C, the user selects "ant design" among the plurality of recommended search terms, and thus, corrects the content "design by day" input by the user by mistake to the content "ant design" that the user really intends to input. The accuracy of recommending the search terms is improved, so that the user can conveniently select the content which is really intended to be searched, and the searching efficiency of the user is improved.

Fig. 5 shows a block diagram of a recommendation device according to an exemplary embodiment of the disclosure, and as shown in fig. 5, the recommendation device 500 includes: a receiving unit 501 configured to receive a plurality of characters input in a search box; a first determining unit 502 configured to determine, for a set of consecutive characters of a plurality of characters, recognition results of at least two different languages corresponding to the consecutive characters; and a second determining unit 503 configured to determine recommended search terms for the plurality of characters based on the recognition results of the at least two different languages.

According to some embodiments, the consecutive characters are consecutive chinese characters, and the consecutive characters are determined based on a plurality of letters input, and wherein the first determining unit includes: a subunit for determining the continuous characters themselves as Chinese recognition results corresponding to the continuous characters; and a subunit for determining a plurality of letters for determining the continuous characters as English recognition results corresponding to the continuous characters.

According to some embodiments, the consecutive characters are consecutive alphabetic characters, and wherein the first determining unit includes: a subunit for determining the continuous character itself as the English recognition result corresponding to the continuous character; and a subunit for determining a Chinese recognition result corresponding to each of the consecutive characters based on the Pinyin represented by each of the consecutive characters.

According to some embodiments, the second determination unit comprises: the first determining subunit is configured to determine, for each of the recognition results of at least two different languages, a plurality of candidate search terms corresponding to the recognition result; a ranking subunit configured to rank the plurality of candidate search terms corresponding to each of the at least two recognition results to determine a ranked result; and a second determining subunit configured to determine, based on the sorting result, a recommended search term for the plurality of characters.

According to some embodiments, the first determining subunit comprises: a subunit, configured to perform semantic analysis on the recognition result, and determine at least one recommendation expression corresponding to the recognition result; and a subunit, configured to generate, based on the at least one recommendation expression, a plurality of candidate search terms corresponding to the recognition result, where each candidate search term includes one of the at least one recommendation expression.

According to some embodiments, the ordering subunit comprises: a subunit for performing ranking based on a degree of association between each candidate search term and the user representation.

According to an embodiment of the present disclosure, there is also provided an electronic apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform any one of the methods described above.

There is also provided, in accordance with an embodiment of the present disclosure, a non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to perform any of the methods described above.

There is also provided, in accordance with an embodiment of the present disclosure, a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements any of the methods described above.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

Referring to fig. 6, a block diagram of a structure of an electronic device 600, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 608 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and/or a chipset, such as a bluetooth (TM) device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and/or the like.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 executes the respective methods and processes described above, such as the recommendation method. For example, in some embodiments, the recommendation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the recommendation method in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims (15)

1. A recommendation method, comprising:

receiving a plurality of characters input into a search box;

determining recognition results of at least two different languages corresponding to a group of continuous characters in the plurality of characters; and

determining recommended search terms for the plurality of characters based on the recognition results of the at least two different languages.

2. The method of claim 1, wherein the consecutive characters are consecutive chinese characters and the consecutive characters are determined based on a plurality of letters entered, and wherein the determining recognition results of at least two different languages corresponding to the consecutive characters comprises:

determining the continuous characters as Chinese recognition results corresponding to the continuous characters; and

and determining a plurality of letters used for determining the continuous characters as English recognition results corresponding to the continuous characters.

3. The method of claim 1, wherein the consecutive characters are consecutive alphabetic characters, and wherein the determining recognition results of at least two different languages corresponding to the consecutive characters comprises:

determining the continuous characters as English recognition results corresponding to the continuous characters; and

and determining a Chinese recognition result corresponding to each continuous character based on the Pinyin represented by each continuous character.

4. The method of any of claims 1-3, wherein determining the recommended search terms for the plurality of characters based on the recognition results of the at least two different languages comprises:

aiming at each recognition result of the at least two different languages, determining a plurality of candidate search terms corresponding to the recognition result;

ranking the plurality of candidate search terms corresponding to each of the at least two recognition results to determine a ranking result; and

based on the ranking results, a recommended search term for the plurality of characters is determined.

5. The method according to claim 4, wherein the determining, for each of the at least two different languages of recognition results, a plurality of candidate search terms corresponding to the recognition result comprises:

determining at least one recommendation expression corresponding to the recognition result by performing semantic analysis on the recognition result; and

and generating a plurality of candidate search terms corresponding to the identification result based on the at least one recommendation expression, wherein each candidate search term comprises one of the at least one recommendation expression.

6. The method of claim 4, wherein the ranking the plurality of candidate search terms for each of the at least two recognition results comprises:

the ranking is performed based on a degree of association between each candidate search term and the user representation.

7. A recommendation device, comprising:

a receiving unit configured to receive a plurality of characters input in a search box;

a first determination unit configured to determine, for a group of consecutive characters of the plurality of characters, recognition results of at least two different languages corresponding to the consecutive characters; and

a second determination unit configured to determine recommended search terms for the plurality of characters based on the recognition results of the at least two different languages.

8. The apparatus according to claim 7, wherein the consecutive characters are consecutive chinese characters, and the consecutive characters are determined based on a plurality of letters input, and wherein the first determining unit includes:

the subunit is used for determining the continuous characters as Chinese recognition results corresponding to the continuous characters; and

and the subunit is used for determining a plurality of letters used for determining the continuous characters as English recognition results corresponding to the continuous characters.

9. The apparatus of claim 7, wherein the consecutive characters are consecutive alphabetic characters, and wherein the first determining unit comprises:

the subunit is used for determining the continuous characters as English recognition results corresponding to the continuous characters; and

and the subunit is used for determining the Chinese recognition result corresponding to each continuous character based on the Pinyin represented by the continuous character.

10. The apparatus according to any one of claims 7 to 9, wherein the second determining unit comprises:

the first determining subunit is configured to determine, for each of the recognition results of the at least two different languages, a plurality of candidate search terms corresponding to the recognition result;

a ranking subunit configured to rank the plurality of candidate search terms corresponding to each of the at least two recognition results to determine a ranking result; and

a second determining subunit configured to determine a recommended search term for the plurality of characters based on the ranking result.

11. The apparatus of claim 10, wherein the first determining subunit comprises:

a subunit, configured to perform semantic analysis on the recognition result, and determine at least one recommendation expression corresponding to the recognition result; and

and the subunit is used for generating a plurality of candidate search terms corresponding to the identification result based on the at least one recommendation expression, wherein each candidate search term comprises one of the at least one recommendation expression.

12. The apparatus of claim 10, wherein the ordering subunit comprises:

a subunit for performing the ranking based on a degree of association between each candidate search term and the user representation.

13. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-6 when executed by a processor.

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