CN107291835B - Search term recommendation method and device - Google Patents
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- CN107291835B CN107291835B CN201710397412.7A CN201710397412A CN107291835B CN 107291835 B CN107291835 B CN 107291835B CN 201710397412 A CN201710397412 A CN 201710397412A CN 107291835 B CN107291835 B CN 107291835B
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- G06F16/9535—Search customisation based on user profiles and personalisation
Abstract
The invention discloses a recommendation method and device for search terms, and relates to the technical field of computers. One embodiment of the method comprises: acquiring search terms input by a user and product information clicked in the search term query result by the user; according to the product information, all search terms capable of searching the product information are obtained, and similarity values of the obtained search terms and the search terms input by the user are respectively calculated; and recommending the obtained search terms in sequence according to the similarity value. The embodiment can perform search recommendation in a real sense according to the search words input by the user.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for recommending search terms.
Background
With the rapid development of internet technology, service providers provide more and more humanized services to users. In the internet service, when a user inputs a search word in the internet to search, a service provider also understands the search intention of the user when presenting relevant search results to the user. When the search result can not meet the search purpose of the user, the workload of inputting new words by the user is reduced by providing the search words related to the search words of the user; meanwhile, the new words are displayed to attract the user to conduct further searching, and the user loss is avoided.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the existing search service providers only simply recommend similar words according to the words of the search words input by users, and search recommendation in a real sense is not realized.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for recommending search terms, which can perform search recommendation in a true sense according to a search term input by a user.
In order to achieve the above object, according to an aspect of the embodiments of the present invention, a method for recommending a search term is provided, including obtaining a search term input by a user and product information clicked by the user in a search term query result; according to the product information, all search terms capable of searching the product information are obtained, and similarity values of the obtained search terms and the search terms input by the user are respectively calculated; and recommending the obtained search terms in sequence according to the similarity value.
Optionally, before sequentially recommending the obtained search terms according to the similarity value, the method further includes: acquiring product information of a model different from the product information; obtaining all search terms capable of searching the product information of different models according to the product information of different models; and respectively calculating similarity values of all search terms of the product information of different models and the search terms input by the user.
Optionally, before sequentially recommending the obtained search terms according to the similarity value, the method further includes: determining that the similarity value between the search words of the product information of different models and the search words input by the user is greater than a preset similarity threshold value; and aggregating the search words of the product information of different models with the search words of the product information, and sequentially recommending the obtained aggregated search words according to the similarity value of the aggregated search words and the search words input by the user.
Optionally, the separately calculating similarity values of the obtained search terms and the search terms input by the user includes: and according to a preset hierarchical structure, carrying out hierarchical classification on the search word and the search word input by the user, and then calculating the similarity value of the search word and the search word input by the user.
Optionally, before sequentially recommending the obtained search terms according to the similarity values, the method further includes: attenuating the similarity value to obtain an attenuated similarity value; and recommending the obtained search terms in sequence according to the attenuated similarity value.
Optionally, before the obtaining of the product information clicked by the user in the search term query result, the method further includes: and carrying out normalization processing on the acquired search terms input by the user.
According to another aspect of the embodiment of the present invention, there is also provided a search term recommendation apparatus, including an obtaining module, configured to obtain a search term input by a user and product information clicked by the user in a search term query result; the calculation module is used for acquiring all search terms capable of searching the product information according to the product information and respectively calculating similarity values of the acquired search terms and the search terms input by the user; and the recommending module is used for sequentially recommending the obtained search terms according to the similarity value.
Optionally, the computing module is further configured to: acquiring product information of a model different from the product information; obtaining all search terms capable of searching the product information of different models according to the product information of different models; and respectively calculating similarity values of all search terms of the product information of different models and the search terms input by the user.
Optionally, before the recommending module sequentially recommends the obtained search terms according to the similarity value, the recommending module is further configured to: determining that the similarity value between the search words of the product information of different models and the search words input by the user is greater than a preset similarity threshold value; and aggregating the search words of the product information of different models with the search words of the product information, and sequentially recommending the obtained aggregated search words according to the similarity value of the aggregated search words and the search words input by the user.
Optionally, the calculating module calculates similarity values between the obtained search terms and the search terms input by the user respectively, and includes: and according to a preset hierarchical structure, carrying out hierarchical classification on the search word and the search word input by the user, and then calculating the similarity value of the search word and the search word input by the user.
Optionally, before the recommending module sequentially recommends the obtained search terms according to the similarity value, the recommending module is further configured to: attenuating the similarity value to obtain an attenuated similarity value; and recommending the obtained search terms in sequence according to the attenuated similarity value.
Optionally, before obtaining the product information clicked by the user in the search term query result, the obtaining module is further configured to: and carrying out normalization processing on the acquired search terms input by the user.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any of the embodiments described above.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: because the technical means of searching for the search terms and recommending the search terms according to the product information clicked by the user in the search term query result is adopted, the technical problem that search and recommendation in a real sense are not realized is solved, and the technical effect of effectively and quickly recommending the search terms for the user is further achieved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 2 is a schematic diagram of a main flow of a recommendation method of a search term according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a main flow of a recommendation method of a search term according to a referential embodiment of the present invention;
fig. 4 is a schematic diagram of the main blocks of an apparatus for recommending search terms according to an embodiment of the present invention;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention 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 and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 illustrates an exemplary system architecture 100 to which a search term recommendation method or a search term recommendation apparatus according to an embodiment of the present invention may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for shopping-like websites browsed by users using the terminal devices 101, 102, 103. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the recommendation method for search terms provided by the embodiment of the present invention is generally executed by the server 105, and accordingly, the recommendation device for search terms is generally disposed in the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 is a method for recommending a search term according to an embodiment of the present invention, and as shown in fig. 2, the method for recommending a search term includes:
step S201, obtaining the search word input by the user and the product information clicked by the user in the search word query result.
As an embodiment, a search word input by a user may be obtained, and in some cases, when the user inputs the search word, problems of confusion, miscase, improper space processing (the space has no space, and the space is not the space but the space), wrong word order, and the like may occur. Therefore, the search term input by the user can be normalized after the search term input by the user is acquired. That is to say, search terms input by a user are sorted, and some obvious errors are modified, so that the obtained search terms have normalization.
In addition, while the search terms input by the user are obtained, the product information clicked in the search term query result by the user also needs to be obtained, that is, the query result is obtained by inputting the search terms for retrieval, and then the product information in the query result is clicked by the user. For example: the search word input by the user is 'refrigerator', and then the information of the product clicked in the query result of the search word 'refrigerator' is 'Haier (Haier) BCD-258WDPM 258 liter air-cooled frostless tri-door refrigerator'.
Step S202, obtaining all search terms capable of searching the product information according to the product information, and respectively calculating similarity values of the obtained search terms and the search terms input by the user.
In an embodiment, all search terms capable of searching the product information can be obtained by backward pushing according to the product information clicked by the user. For example: the superior information of the product information can be found as a search word through a preset hierarchical structure, and the method can also be used for searching the historical record of the product information and the like. Preferably, in order to obtain a more accurate search term of the product information, the search term corresponding to the product information clicked by the user may be searched in a history record according to the product information clicked by the user. The historical records store the mapping relation between the product information clicked by the user and the search terms input by the user. For example: the history records store a search word corresponding to the product information of 'Haier BCD-258WDPM 258 liter air-cooled frostless tri-door refrigerator' as 'Haier refrigerator', and a corresponding search word as 'tri-door refrigerator'.
As a preferred embodiment, when calculating the similarity between the obtained search term and the search term input by the user, the search term and the search term input by the user may be hierarchically classified according to a preset hierarchical structure, and then the similarity between the search term and the search term input by the user is calculated. For example: the search word is 'refrigerator', the obtained search word is 'hell refrigerator', the 'refrigerator' is in three levels, the 'hell refrigerator' is in four levels according to a preset hierarchical structure, and then the similarity value score between the 'refrigerator' and the 'hell refrigerator' is 1.
As a further embodiment of step S202, in order to provide more search terms to the user, product information of a different model from the product information may be acquired at the same time as the search term corresponding to the product information is acquired. Then, according to the product information of different models, all search terms capable of searching the product information of different models are obtained, and similarity values of all the search terms of the product information of different models and the search terms input by the user are calculated respectively. For example: the product information of different models from the product information 'Haier BCD-258WDPM 258 liter air-cooled frostless tri-door refrigerator' is 'Haier BCD-248WDPM 248 liter air-cooled frostless tri-door refrigerator'.
And step S203, recommending the obtained search terms in sequence according to the similarity value.
In the embodiment, in order to recommend more effective and accurate search terms to the user, the obtained similarity values may be screened first and then recommended. Preferably, a similarity threshold may be preset, and it is determined whether the obtained similarity value is greater than the similarity threshold, and if the obtained similarity value is greater than the similarity threshold, recommendation is performed, and if the obtained similarity value is less than or equal to the similarity threshold, recommendation is not performed. It should be noted that recommending search terms with similarity values larger than the similarity threshold value enables users to search for more products when using the search terms to query, and enables users to have more choices. Further, when the search term recommendation is performed, the corresponding search terms may be sequentially recommended according to the descending order of the similarity values.
In another preferred embodiment, it is determined whether the similarity value between the search term of the product information of different models and the search term input by the user is greater than a preset similarity threshold, and if so, the search term of the product information of different models and the search term of the product information are aggregated, and then the obtained aggregated search terms are sequentially recommended according to the similarity value between the aggregated search term and the search term input by the user. And if the similarity value between the search word of the product information of different types and the search word input by the user is smaller than or equal to a preset similarity threshold value, discarding the search word of the product information of different types. The search words of the product information of different models and the search words of the product information are aggregated, for example, the "Haier BCD-258WDPM 258L air-cooled frostless tri-door refrigerator" and the "Haier BCD-248WDPM 248L air-cooled frostless tri-door refrigerator" can pass through the search words of the "tri-door refrigerator", so that the two "tri-door refrigerators" can be combined into one "tri-door refrigerator".
In a preferred embodiment, in order to avoid some characteristic situations causing inaccuracy of the calculated similarity value (for example, some category of promotion activity causes abnormality of part of the query words), the similarity value obtained at this time may be attenuated, and the attenuated similarity value is obtained. And then recommending the obtained search terms in sequence according to the attenuated similarity value. Further, the attenuation factor is: a-b + pow (day _ pass, c). Wherein a represents an initial weight (preferably, a has a value of 1 or 2); b (preferably, b has a value of 1) and c (preferably, c has a value of 0.08) both represent the degree of attenuation, the greater the values of b and c, the greater the attenuation; two parameters are used because the decay rates corresponding to the two parameters are different; day _ pass represents the number of days elapsed. Then, the obtained similarity value is multiplied by the attenuation factor to obtain an attenuated similarity value.
Fig. 3 is a schematic diagram of a main flow of a method for recommending search terms according to a referential embodiment of the present invention, and the method for recommending search terms may include:
step S301, acquiring the search word input by the user, and performing normalization processing on the search word input by the user.
Step S302, product information clicked by the user in the search term query result is obtained.
Step S303, obtaining all search terms capable of searching the product information according to the product information, and respectively calculating similarity values of the obtained search terms and the search terms input by the user.
It should be noted that step S303 is executed simultaneously with step S304 and step S305, or step S303 may be executed first and then steps S304 and S305 may be executed, or steps S304 and S305 may be executed first and then step S303 may be executed.
Preferably, in step S303, the search terms and the search terms input by the user may be hierarchically classified according to a preset hierarchical structure, and then a similarity value between the search terms and the search terms input by the user is calculated.
In another preferred embodiment, in step S303, according to the product information clicked by the user, the search term corresponding to the clicked product information is searched in the history. The historical records store the mapping relation between the product information clicked by the user and the search terms input by the user.
Step S304, obtaining product information with different models from the product information, obtaining all search terms capable of searching the product information with different models according to the product information with different models, and respectively calculating similarity values of all search terms of the product information with different models and the search terms input by the user.
Step S305, determining whether the similarity between the search term of the product information of different models and the search term input by the user is greater than a preset similarity threshold, if so, performing step S306, and if not, performing step S307.
Step S306, aggregating the search terms of the product information of different models and the search terms of the product information, and then executing step S308.
And step S307, discarding the search terms of the product information of different models, and then exiting the process.
Step S308, attenuating the obtained similarity value of the aggregated search word to obtain an attenuated similarity value.
And step S309, recommending the obtained search terms in sequence according to the descending order of the similarity values after attenuation.
In addition, the present invention may refer to the detailed implementation contents of the recommendation method of the search term in the embodiment, which have been described in detail in the above recommendation method of the search term, so that the repeated contents are not described again.
Fig. 4 is a device for recommending search terms according to an embodiment of the present invention, and as shown in fig. 4, the device 400 for recommending search terms includes an obtaining module 401, a calculating module 402, and a recommending module 403. The obtaining module 401 obtains a search term input by a user and product information clicked by the user in the search term query result. The calculation module 402 obtains all search terms that can be searched for the product information according to the product information, and calculates similarity values between the obtained search terms and the search terms input by the user, respectively. Finally, the recommending module 403 sequentially recommends the obtained search terms according to the similarity value.
Preferably, the obtaining module 401 may obtain the search word input by the user, and in some cases, when the user inputs the search word, problems of confusion, miscase, mishandling of the blank (the blank has no blank, and the blank is not a blank but a blank), wrong word order, and the like may occur, and if the search word input by the user is directly subjected to subsequent processing, the last recommended search word may be inaccurate. Therefore, the search term input by the user can be normalized after the search term input by the user is acquired.
In a preferred embodiment, in order to provide more search terms to the user, the calculation module 402 may obtain the product information of a different model from the product information while obtaining the search term corresponding to the product information. Then, according to the product information of different models, all search terms capable of searching the product information of different models are obtained, and similarity values of all the search terms of the product information of different models and the search terms input by the user are calculated respectively.
As an embodiment, the calculation module 402 may obtain all search terms capable of searching the product information by reverse pushing according to the product information clicked by the user. For example: the superior information of the product information can be found as a search word through a preset hierarchical structure, and the method can also be used for searching the historical record of the product information and the like. Preferably, in order to obtain a more accurate search term of the product information, the search term corresponding to the product information clicked by the user may be searched in a history record according to the product information clicked by the user. The historical records store the mapping relation between the product information clicked by the user and the search terms input by the user.
In addition, in order to recommend a more effective and accurate search term to the user, the recommending module 403 may first filter the obtained similarity value and then recommend the search term. Preferably, a similarity threshold may be preset, and it is determined whether the obtained similarity value is greater than the similarity threshold, and if the obtained similarity value is greater than the similarity threshold, recommendation is performed, and if the obtained similarity value is less than or equal to the similarity threshold, recommendation is not performed. Further, when the search term recommendation is performed, the corresponding search terms may be sequentially recommended according to the descending order of the similarity values.
In addition, the recommending module 403 may further determine whether the similarity value between the search term of the product information of different models and the search term input by the user is greater than a preset similarity threshold, aggregate the search term of the product information of different models and the search term of the product information if the similarity value is greater than the preset similarity threshold, and then sequentially recommend the obtained aggregated search terms according to the similarity value between the aggregated search term and the search term input by the user. And if the similarity value between the search word of the product information of different types and the search word input by the user is smaller than or equal to a preset similarity threshold value, discarding the search word of the product information of different types.
As another example, in order to avoid some characteristic situations causing inaccuracy of the calculated similarity value, the recommending module 403 may attenuate the similarity value obtained at this time to obtain an attenuated similarity value. And then recommending the obtained search terms in sequence according to the attenuated similarity value. Further, the attenuation factor is: a-b + pow (day _ pass, c). Wherein a represents an initial weight (preferably, a has a value of 1 or 2); b (preferably, b has a value of 1) and c (preferably, c has a value of 0.08) both represent the degree of attenuation, the greater the values of b and c, the greater the attenuation; two parameters are used because the decay rates corresponding to the two parameters are different; day _ pass represents the number of days elapsed. Then, the obtained similarity value is multiplied by the attenuation factor to obtain an attenuated similarity value.
It should be noted that, the detailed implementation contents of the apparatus for recommending search terms according to the present invention have been described in detail in the above method for recommending search terms, and therefore, the repeated contents are not described herein.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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 portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a calculation module, and a recommendation module. The names of these modules do not constitute a limitation to the modules themselves in some cases, and for example, the acquisition module may also be described as a "module that transmits a search term acquisition request input by a user to a connected terminal".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring search terms input by a user and product information clicked in the search term query result by the user; according to the product information, all search terms capable of searching the product information are obtained, and similarity values of the obtained search terms and the search terms input by the user are respectively calculated; and recommending the obtained search terms in sequence according to the similarity value.
According to the technical scheme of the embodiment of the invention, the search terms can be searched and recommended according to the product information clicked by the user in the search term query result, so that the search terms can be effectively and quickly recommended for the user.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method for recommending search terms, comprising:
acquiring search terms input by a user and product information clicked in the search term query result by the user;
according to the product information, all search terms capable of searching the product information are obtained, and similarity values of the obtained search terms and the search terms input by the user are respectively calculated;
sequentially recommending the obtained search terms according to the similarity value;
wherein the respectively calculating similarity values of the obtained search terms and the search terms input by the user comprises:
and according to a preset hierarchical structure, carrying out hierarchical classification on the search word and the search word input by the user, and then calculating the similarity value of the search word and the search word input by the user.
2. The method of claim 1, wherein before sequentially recommending the obtained search terms according to the similarity value, further comprising:
acquiring product information of a model different from the product information;
obtaining all search terms capable of searching the product information of different models according to the product information of different models;
and respectively calculating similarity values of all search terms of the product information of different models and the search terms input by the user.
3. The method of claim 2, wherein before sequentially recommending the obtained search terms according to the similarity value, further comprising:
determining that the similarity value between the search words of the product information of different models and the search words input by the user is greater than a preset similarity threshold value;
and aggregating the search words of the product information of different models with the search words of the product information, and sequentially recommending the obtained aggregated search words according to the similarity value of the aggregated search words and the search words input by the user.
4. The method of claim 1, wherein before recommending the obtained search terms in order according to the similarity value, further comprising:
attenuating the similarity value to obtain an attenuated similarity value;
and recommending the obtained search terms in sequence according to the attenuated similarity value.
5. The method according to any one of claims 1-4, further comprising, before the obtaining product information clicked by the user in the search term query result:
and carrying out normalization processing on the acquired search terms input by the user.
6. An apparatus for recommending search terms, comprising:
the acquisition module is used for acquiring search terms input by a user and product information clicked by the user in the search term query result;
the calculation module is used for acquiring all search terms capable of searching the product information according to the product information and respectively calculating similarity values of the acquired search terms and the search terms input by the user; wherein, respectively calculating the similarity values of the obtained search terms and the search terms input by the user comprises:
according to a preset hierarchical structure, carrying out hierarchical classification on the search terms and the search terms input by the user, and then calculating the similarity value of the search terms and the search terms input by the user;
and the recommending module is used for sequentially recommending the obtained search terms according to the similarity value.
7. The apparatus of claim 6, wherein the computing module is further configured to:
acquiring product information of a model different from the product information;
obtaining all search terms capable of searching the product information of different models according to the product information of different models;
and respectively calculating similarity values of all search terms of the product information of different models and the search terms input by the user.
8. The apparatus of claim 7, wherein before the recommending module sequentially recommends the obtained search terms according to the similarity value, the recommending module is further configured to:
determining that the similarity value between the search words of the product information of different models and the search words input by the user is greater than a preset similarity threshold value;
and aggregating the search words of the product information of different models with the search words of the product information, and sequentially recommending the obtained aggregated search words according to the similarity value of the aggregated search words and the search words input by the user.
9. The apparatus of claim 6, wherein before the recommending module sequentially recommends the obtained search terms according to the similarity value, the recommending module is further configured to:
attenuating the similarity value to obtain an attenuated similarity value;
and recommending the obtained search terms in sequence according to the attenuated similarity value.
10. The apparatus according to any one of claims 6-9, wherein the obtaining module, before obtaining the product information clicked by the user in the search term query result, is further configured to:
and carrying out normalization processing on the acquired search terms input by the user.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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| CN111475729B (en) * | 2020-04-07 | 2023-07-25 | 腾讯科技（深圳）有限公司 | Search content recommendation method and device |
| CN111538903B (en) * | 2020-04-17 | 2023-03-31 | 抖音视界有限公司 | Method and device for determining search recommended word, electronic equipment and computer readable medium |
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