Commodity searching method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a commodity search method and device, electronic equipment and storage medium, and relates to the technical field of electronic commerce. The method comprises the following steps: obtaining a commodity search request of a user; performing lexical analysis on the commodity search request to obtain a search word; searching in a first search system according to the search word to obtain a first search result; returning the first search result to the user to search in a second search system according to the selection of the user on the first search result. The present disclosure can accurately search for commodities.

Description

Technical Field

[0001] This disclosure relates to the field of e-commerce technology, and more specifically, to a product search method, a product search device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] With the rapid development of e-commerce, more and more people are shopping online. For those who are not familiar with online shopping, how to quickly find the products they need is an urgent problem to be solved.

[0003] To address the aforementioned issues, related technologies, upon receiving a user's purchase intent, can search for multiple products directly related to that intent, but cannot search for other products with less relevance. If the user's desired product type is not found among the search results, the user needs to further identify all relevant product types through a third-party search engine before selecting a satisfactory product from the shopping website.

[0004] Therefore, the above methods cannot achieve comprehensive and accurate product search; in addition, they cannot search for products that satisfy users according to their purchasing intentions, thus reducing user satisfaction and user experience.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a product search method, apparatus, electronic device, and storage medium, thereby overcoming, to at least a certain extent, the problem of inaccurate product search caused by limitations and defects in related technologies.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to one aspect of this disclosure, a product search method is provided, comprising: obtaining a user's product search request; performing lexical analysis on the product search request to obtain search terms; performing a search in a first search system based on the search terms to obtain a first search result; and returning the first search result to the user to perform a search in a second search system based on the user's selection of the first search result.

[0009] In one exemplary embodiment of this disclosure, performing lexical analysis on the product search request to obtain search terms includes: converting the string in the product search request into words; and / or separating the words from the sentence of the product search request.

[0010] In one exemplary embodiment of this disclosure, performing lexical analysis on the product search request to obtain search terms includes: analyzing the morpheme components of the words and determining the search terms based on the morpheme components.

[0011] In one exemplary embodiment of this disclosure, performing a search in a first search system based on the search term to obtain a first search result includes: if the morpheme component is a product name, then performing a search through the first search system and returning the first search result as a first data set.

[0012] In one exemplary embodiment of this disclosure, searching in a first search system based on the search term to obtain a first search result includes: if the morpheme component is not a product name, determining whether the morpheme component is a product attribute; if the morpheme component is a product attribute, searching through the first search system and returning the first search result as a second data set.

[0013] In one exemplary embodiment of this disclosure, the method further includes providing an error message if the morpheme component is not a product attribute.

[0014] In one exemplary embodiment of this disclosure, the method further includes: if the morpheme component includes a product name and a product attribute, then determining the word as the search term; and inputting the search term into the second search system for searching.

[0015] According to one aspect of this disclosure, a product search device is provided, comprising: a request acquisition module for acquiring a user's product search request; a lexical analysis module for performing lexical analysis on the product search request to obtain search terms; a first search module for performing a search in a first search system based on the search terms to obtain a first search result; and a second search module for returning the first search result to the user to perform a search in a second search system based on the user's selection of the first search result.

[0016] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the product search method described in any one of the preceding claims by executing the executable instructions.

[0017] According to one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the product search method described in any of the preceding claims.

[0018] In the exemplary embodiments of this disclosure, a product search method, a product search device electronic device, and a computer-readable storage medium are provided. Search terms are obtained by performing lexical analysis on a product search request, and a first search result is obtained in a first search system based on the search terms. Then, a search is performed in a second search system based on the first search result. On the one hand, by obtaining the first search result in the first search system and then performing a search in the second search system based on the first search result, comprehensive and refined product searching can be achieved. On the other hand, through two-layer searching, accurate products can be obtained, improving user satisfaction and user experience. Furthermore, the step of users querying products related to the product search request is avoided, improving operational convenience and efficiency.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0021] Figure 1 This illustration schematically shows a product search method according to an exemplary embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram illustrating a knowledge graph in an exemplary embodiment of this disclosure is shown.

[0023] Figure 3 This schematically illustrates a flowchart of the first search result acquisition process in an exemplary embodiment of the present disclosure;

[0024] Figure 4 This schematically illustrates a specific flowchart of a product search method in an exemplary embodiment of this disclosure;

[0025] Figure 5 This schematic diagram illustrates a block diagram of a product search device according to an exemplary embodiment of the present disclosure;

[0026] Figure 6 A block diagram schematically illustrating an electronic device according to an exemplary embodiment of the present disclosure;

[0027] Figure 7 An exemplary embodiment of the present disclosure is illustrated schematically. Detailed Implementation

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0029] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0030] This example implementation first provides a product search method that can be applied to product search scenarios on major e-commerce platforms. (Reference) Figure 1 As shown, this product search method may include the following steps:

[0031] In step S110, the user's product search request is obtained;

[0032] In step S120, lexical analysis is performed on the product search request to obtain search terms;

[0033] In step S130, a search is performed in the first search system based on the search term to obtain a first search result;

[0034] In step S140, the first search result is returned to the user so that a search can be performed in the second search system based on the user's selection of the first search result.

[0035] In the product search method provided in this exemplary embodiment, on the one hand, by obtaining a first search result in a first search system and then searching in a second search system based on the first search result, a comprehensive and refined search for products can be achieved; on the other hand, through two-layer search, accurate products can be obtained, improving user satisfaction and user experience; and on the other hand, the steps of searching for products by the user are avoided, improving operational convenience and operational efficiency.

[0036] Next, the product search method in this exemplary embodiment will be further explained with reference to the accompanying drawings.

[0037] In step S110, the user's product search request is obtained.

[0038] In this example, the server can obtain product search requests entered by users through various terminals such as smartphones and computers in the search box position on various e-commerce platforms, e-commerce websites, or APP pages. Users can enter product search requests in the form of text, voice, or photos. Specifically, product search requests can be generated in various ways, such as by obtaining text entered by the user using a keyboard or touchscreen, or by recognizing the user's voice. This exemplary embodiment does not impose any special limitations on this.

[0039] Product search requests can include fuzzy search requests from users who are unfamiliar with online shopping, as well as precise search requests, such as "what to eat to supplement calcium", "calcium supplementation for health", or "milk, brand A", etc.

[0040] In step S120, lexical analysis is performed on the product search request to obtain search terms.

[0041] In this example, lexical analysis refers to the process of converting a sequence of characters into a sequence of words. Generally, a lexical analyzer can identify various types of words according to the lexical rules of a language and generate the corresponding attribute words. Specifically, lexical analysis can be performed by a program or function. Through lexical analysis, the type of the product search request can be determined, such as whether it is a string, a sentence, a word, or a combination of these.

[0042] The search terms here can be the specific names of products or services, or the words the user actually searches for. For example, if a user wants to buy an MP3 player online, they might enter search terms such as "MP3," "MP3 player," "player," or "walkman" into the browser's address bar or search box to find related products on shopping websites. It should be noted that there may be multiple search terms for the same product; or one search term may correspond to multiple products.

[0043] In this example, by performing lexical analysis on the obtained product search requests, the search terms corresponding to each product search request can be obtained. For example, the search term for the product search request "What to eat to supplement calcium" can be "supplement calcium"; the search terms for the product search request "milk, brand A" can be "milk" or "brand A".

[0044] Specifically, performing lexical analysis on the product search request may include: converting the string in the product search request into words; and / or separating the words in the sentence of the product search request. That is to say, when the product search request includes a string, a program can be written to convert it into a word string, for example, converting "milk" into "牛奶". If the product search request includes a sentence, at least one word can be separated from the sentence according to the grammar rules. For example, from the product search request "What food to eat to supplement calcium", the two words "food" and "supplement calcium" are separated. The process of converting a string into words and separating words from a sentence can be implemented separately, but when the product search request contains both a string and a sentence at the same time, these two processes can be implemented simultaneously.

[0045] Next, the morpheme components of the words obtained by string conversion or the words separated from the sentence can be analyzed, and the search terms can be determined according to the morpheme components. Among them, a morpheme is the smallest meaningful language unit. The morpheme components in this example may only include product names or product attributes, and may also include product names and product attributes at the same time. Next, according to different morpheme components, the search terms corresponding to the product search requests input by the user can be determined.

[0046] In step S130, search in the first search system according to the search terms to obtain the first search result.

[0047] Among them, the first search system can be the knowledge graph corresponding to the shopping website. The knowledge graph is used to describe concepts and their interrelationships in symbolic form, and the knowledge graph is established in advance in a bottom-up or top-down manner. The knowledge graphs in different fields are different. Therefore, in this example, only the knowledge graph in the e-commerce field, for example, needs to be obtained and directly used.

[0048] The basic unit of composition of the knowledge graph is the triple of entity-relationship-entity and the entity-attribute-value pair. Among them, entities are connected to each other through relationships to form a networked knowledge structure. The knowledge graph stores structured data, and a graph database is used at the bottom layer. Among them, the internal storage of the graph database uses an adjacency matrix or an adjacency list. It should be noted that the entities in this example refer to products.

[0049] By using knowledge graphs to search for products using search terms, more diverse and comprehensive product search results can be obtained, improving the depth and breadth of the search. At the same time, it also avoids the problem of users not being able to find the search results they want, thus improving user satisfaction with product searches.

[0050] The first search result can be obtained from the knowledge graph based on the search term, and it can include various different types of results. Specifically, the first search result can be obtained by searching the knowledge graph corresponding to the e-commerce field based on the search term determined in step S120. For example, the search term "calcium supplement" in the knowledge graph for the product search request "what to eat to supplement calcium" can be searched to obtain a first search result that is related to or contains the search term. For example, refer to... Figure 2 As shown, knowledge graphs in the e-commerce field can be used to find various types of products corresponding to the search term "calcium supplement," such as calcium tablets and liquid calcium; milk, such as pure milk and yogurt; soy products; and seafood, such as shrimp. This enables comprehensive product search, avoids the problem of users not being able to find the products they want, and indirectly improves user satisfaction with online shopping.

[0051] Specifically, refer to Figure 3 As shown, upon receiving a user's product search request, the request can be transmitted to the merge server cluster; further, it can be transmitted to the search server cluster; and still further, the product search request processed by the search server cluster can be transmitted to the data platform. The data platform can include databases (DBs). Product search requests from multiple DBs are merged across multiple tables and indexed based on the Hadoop big data platform to achieve incremental indexing. Through this process, a preliminary search is achieved in the first search system.

[0052] After determining the morpheme component, a search is performed in the first search system based on the search term to obtain the first search result. Specifically, this may include: if the morpheme component is a product name, then a search is performed through the first search system and the first search result is returned as a first data set.

[0053] The product name here can be a generic name or a specific name for the product. For example, the morpheme could be "milk" or "A milk," etc. If the morpheme is identified as a product name, it can be entered into the first search system for a preliminary search, yielding the first search result matching the product name. For instance, when the morpheme is "milk," products like "A milk" and "B milk" can be found in the knowledge graph. Subsequently, the products corresponding to the first search result can be returned to the user as the first data set. Here, the first data set refers to the product-attribute data set. The product-attribute data set includes a product name and multiple attributes. These attributes are multiple clusters of different types retrieved from the knowledge graph, such as brand attributes, model attributes, color attributes, etc. In this way, the attributes of the first search result can include multiple dimensions.

[0054] In addition, the process of searching the first search system based on the search term to obtain the first search result may also include: if the morpheme is not a product name, then determining whether the morpheme is a product attribute; if the morpheme is a product attribute, then searching through the first search system and returning the first search result as a second data set.

[0055] Product attributes can include brand, shelf life, quantity, price, color, function, purpose, etc. If the analysis shows that the morpheme is not a product name, further analysis can be performed to determine if it is any of the aforementioned product attributes. If it is a product attribute, a preliminary search can be performed using the first search system to obtain the first search result corresponding to the product attribute. Furthermore, the obtained first search result can be returned to the user as a second data set, where the second data set refers to the set of product names. The knowledge graph can retrieve the set of product names based on product attributes. When the number of products is small, the set of product names can be displayed directly; when the number is large, it can be displayed as multiple sets clustered according to product categories. Specifically, the first search result obtained based on the product name can be displayed in list form, or a secondary list in the form of kurem-product name can be added; no special limitations are imposed here.

[0056] For example, when the morpheme is "Brand C", the product attribute "Brand C" can be entered into the first search system for a preliminary search, yielding the first search result that matches the product attribute. For instance, when the morpheme is "Brand C", products such as "Brand C pure milk" and "Brand C yogurt" can be found in the knowledge graph.

[0057] In this exemplary embodiment, a preliminary search using the first search system can quickly filter out most products that the user does not need to search for, thereby enabling the user to quickly find the products they need to search for; at the same time, since most products that the user does not need are filtered out, products can be searched accurately.

[0058] In addition, if the morpheme component is determined not to be a product attribute, an error message is provided. That is, if the morpheme component is neither a product name nor a product attribute, a product search cannot be performed based on the morpheme component. In this case, the server can provide an error message, such as displaying a text message or voice prompt saying "Search error" in the first search system, to prevent the system from performing invalid operations based on erroneous search requests, thereby indirectly improving search efficiency.

[0059] In step S140, the first search result is returned to the user so that a search can be performed in the second search system based on the user's selection of the first search result.

[0060] After obtaining the first search result according to step S130, the first search result can be returned to the user's terminal, such as a smartphone or computer. After the user selects the first search result, they can make another selection in a second search system based on their selection. This second search system can be, for example, the search system of the shopping website itself. In this way, a product search result can be obtained from multiple first search results obtained through the first search system, achieving precise searching based on the product search request, thereby improving user satisfaction and user experience.

[0061] In addition, if the morpheme component includes both the product name and product attributes, then the words corresponding to the product name and product attributes can be directly identified as search terms. Subsequently, a search can be performed directly in the second search system based on the search terms, and the search results obtained are the final products found. In this case, the product the user wants to search for can be accurately determined directly based on the product name and product attributes, so there is no need for preliminary screening based on a knowledge graph.

[0062] For example, if the morpheme is "pure milk + brand C", then "pure milk + brand C" can be identified as the search term and entered into the second search system for searching. At this time, the product that the user needs can be obtained, thus achieving precise search.

[0063] It should be noted that after the user obtains the desired product through both the first and second search systems, or only through the second search system, the e-commerce platform's server can return the search results to the user's terminal and display the final search results on the terminal's shopping page, allowing the user to select the product they are satisfied with.

[0064] As shown above, users can directly input their intentions as a product search request. Through the first and second search systems, the system can quickly identify the products the user wants without requiring additional searches through third-party search engines. For users, this reduces unnecessary operations, saves time, and allows them to obtain the products they need. Therefore, it provides users with a better shopping experience and a more convenient and intelligent shopping method. It also lowers the barrier to entry for users, making it more suitable for the elderly and those who have needs but don't know what products to buy. For the system, it improves the efficiency of product search operations.

[0065] Figure 3 This schematically illustrates the overall flowchart of the product search method in an exemplary embodiment of this disclosure, with reference to... Figure 3 As shown, this product search method mainly includes the following steps:

[0066] S21, the user inputs a product search request through the terminal; for example, inputting a fuzzy or precise product search request through text, voice, or other means;

[0067] S22, product search is performed using the newly added knowledge graph, specifically including:

[0068] S221, The system performs lexical analysis; for example, by using a program or function to perform lexical analysis on the obtained product search request to obtain search terms;

[0069] S222, searching through knowledge graphs; specifically including:

[0070] S2221, Searching is performed using a graph database, that is, storing and querying products using a "graph" data structure;

[0071] S223, Display the results selection interface; for example, return the search results of the knowledge graph to the user's terminal via the network.

[0072] S23, search according to the original product search logic, specifically including:

[0073] S231, Received user input product search request;

[0074] S232, transmit the product search request to the merge server cluster;

[0075] S233, transmit it to the search server cluster;

[0076] S234, the product search requests processed by the search server cluster are transmitted to the data platform. The data platform may include databases (DBs). Product search requests from multiple DBs are merged across multiple tables and indexed using the Hadoop big data platform to achieve incremental indexing. Through this process, a preliminary search is performed in the first search system.

[0077] Figure 4 This schematically illustrates a specific flowchart of a product search method in an exemplary embodiment of this disclosure, with reference to... Figure 4 As shown, the product search method in this example specifically includes the following steps:

[0078] S30, users input product search requests through the terminal; for example, inputting fuzzy or precise product search requests in the form of text, voice, or images;

[0079] S31, The system performs lexical analysis on the acquired product search request through a program or function to obtain search terms;

[0080] S32, determine whether it is a product name and product attribute through lexical analysis. Specifically, determine the morpheme components of the words in the product search request to determine the search terms corresponding to the product search request entered by the user; then determine whether the morpheme components include product name and product attribute; if the morpheme components include product name and product attribute, proceed to S38; if they do not include product name and product attribute, proceed to S34.

[0081] S33, determine whether the morpheme includes a product name; if it does, proceed to S331 and S332; if it does not include a product name or product attributes, proceed to S34; where:

[0082] S331 uses a knowledge graph to search and filter out products that users do not need;

[0083] S332, return the product attribute data set; proceed to S37;

[0084] S34, determine whether the morpheme includes commodity attributes; if it does, proceed to S35; if it does not, proceed to S341; where:

[0085] S341, Search error; for example, provide a text error message in the first search system; proceed to S39;

[0086] S35, searching through a knowledge graph;

[0087] S36, Return of product collection;

[0088] S37, the user makes a selection, for example, the user selects the search results for the knowledge graph;

[0089] S38, the search system performs a search, for example, the search system performs a secondary search based on the knowledge graph, or directly searches based on the product name and product attributes included in the product search request;

[0090] S39, Search results are returned, for example, the search results are returned to the corresponding e-commerce website page on the user's terminal.

[0091] This disclosure also provides a product search device. (See reference) Figure 5 As shown, the product search device 500 may include:

[0092] The request retrieval module 501 can be used to retrieve a user's product search request;

[0093] Lexical analysis module 502 can be used to perform lexical analysis on the product search request to obtain search terms;

[0094] The first search module 503 can be used to perform a search in the first search system based on the search terms to obtain the first search result;

[0095] The second search module 504 can be used to return the first search result to the user so that a search can be performed in the second search system based on the user's selection of the first search result.

[0096] It should be noted that the specific details of each module in the above-mentioned product search device have been described in detail in the corresponding product search methods, so they will not be repeated here.

[0097] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0098] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0099] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0100] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0101] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0102] The following reference Figure 6 To describe an electronic device 600 according to this embodiment of the present invention. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0103] like Figure 6 As shown, the electronic device 600 is manifested in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including storage unit 620 and processing unit 610).

[0104] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1The steps shown are as follows: In step S110, the user's product search request is obtained; in step S120, lexical analysis is performed on the product search request to obtain search terms; in step S130, a search is performed in the first search system according to the search terms to obtain a first search result; in step S140, the first search result is returned to the user so that a search is performed in the second search system according to the user's selection of the first search result.

[0105] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0106] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0107] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0108] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0109] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0110] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0111] refer to Figure 7 As shown, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0112] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0115] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0116] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0117] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A product search method, characterized in that, include: Obtain the user's product search request; Lexical analysis is performed on the product search request to obtain search terms; A search is performed in the first search system based on the search terms to obtain the first search result; the first search system is a knowledge graph corresponding to the shopping website. The first search result is returned to the user, and a search is performed in a second search system based on the user's selection of the first search result; the second search system is the search system of the shopping website itself. The method further includes: Analyze the morpheme components of the words in the product search request, and determine the search terms based on the morpheme components; If the morpheme is a product name, then a search is performed through the first search system and the first search result is returned as a first data set; the first data set is a product-attribute data set. If the morpheme is not a product name, then determine whether the morpheme is a product attribute; If the morpheme is a product attribute, then a search is performed through the first search system and the first search result is returned as a second data set; the second data set is a set of product names. If the morpheme includes the product name and product attributes, then the word is determined as the search term; Enter the search terms into the second search system to perform the search.

2. The product search method according to claim 1, characterized in that, Lexical analysis of the product search request includes: Convert the string in the product search request into words; and / or Extract the words from the sentence of the product search request.

3. The product search method according to claim 1, characterized in that, The method further includes: If the morpheme component is not a product attribute, an error message will be provided.

4. A product search device, characterized in that, include: The request retrieval module is used to retrieve users' product search requests; The lexical analysis module is used to perform lexical analysis on the product search request to obtain search terms; The first search module is used to perform a search in the first search system based on the search terms to obtain the first search result; the first search system is a knowledge graph corresponding to the shopping website. The second search module is used to return the first search result to the user, so as to perform a search in the second search system according to the user's selection of the first search result; the second search system is the search system of the shopping website itself; The lexical analysis module is used to analyze the morpheme components of words in the product search request and determine the search terms based on the morpheme components. The first search module is configured to perform a search through the first search system and return the first search result as a first data set if the morpheme component is a product name; the first data set is a product-attribute data set. If the morpheme is not a product name, then determine whether the morpheme is a product attribute; If the morpheme is a product attribute, then a search is performed through the first search system and the first search result is returned as a second data set; the second data set is a set of product names. The second search module is used to determine the word as the search term if the morpheme component includes the product name and product attributes; Enter the search terms into the second search system to perform the search.

5. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the product search method according to any one of claims 1-3 by executing the executable instructions.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the product search method according to any one of claims 1-3.

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