CN115375385A - Commodity information processing method and device, computer equipment and storage medium - Google Patents
Commodity information processing method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN115375385A CN115375385A CN202110548013.2A CN202110548013A CN115375385A CN 115375385 A CN115375385 A CN 115375385A CN 202110548013 A CN202110548013 A CN 202110548013A CN 115375385 A CN115375385 A CN 115375385A
- Authority
- CN
- China
- Prior art keywords
- commodity
- candidate
- information
- attribute
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
- G06Q30/0643—Graphical representation of items or shoppers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0611—Request for offers or quotes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0623—Item investigation
- G06Q30/0625—Directed, with specific intent or strategy
- G06Q30/0629—Directed, with specific intent or strategy for generating comparisons
Abstract
The disclosure provides a commodity information processing method and device, computer equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: receiving a commodity information query request, wherein the query request comprises commodity keywords; querying a commodity information database to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, wherein the commodity information database comprises the attribute information of each commodity in each commodity platform, and one description information of each candidate commodity comprises the commodity platform where the candidate commodity is located and the attribute information in the commodity platform; determining the same commodity in each candidate commodity according to the attribute information of each candidate commodity; and comparing and displaying the description information of the same commodity. Therefore, by querying the commodity information database, the attribute information of the commodity which the user wants to query in each commodity platform can be determined, and the attribute information is compared and displayed, so that the manual comparison process is reduced.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for processing commodity information, a computer device, and a storage medium.
Background
With the continuous development of society and the remarkable progress of computer technology, the development of the e-commerce industry is more and more rapid. Generally, for the same commodity, the prices of various shopping platforms may have certain gaps, and the user needs price comparison due to cost consideration. In addition, large and medium-sized governments and enterprises often purchase office supplies, industrial supplies and the like on line, and generally the prices of purchased commodities are required to be comprehensively compared with the prices of similar online commodities in consideration of government supervision requirements, enterprise internal audit, cost saving and the like.
However, many e-commerce platforms exist on the line, the commodity system of each e-commerce platform is generally different, and the commodity price is dynamically changed, so that the commodities can be compared only in a small number of ways such as directional spot check of a single channel, and a good solution is not provided for the condition of large-batch multi-channel comprehensive check.
Therefore, how to compare commodities quickly and conveniently to obtain clear and accurate information becomes a problem to be solved urgently at present.
Disclosure of Invention
The present disclosure is directed to solving, at least in part, one of the technical problems in the related art.
An embodiment of a first aspect of the present disclosure provides a method for processing commodity information, including:
receiving a commodity information query request, wherein the query request comprises commodity keywords;
querying a commodity information database to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, wherein the commodity information database comprises attribute information of each commodity in each commodity platform, and one description information of each candidate commodity comprises a commodity platform where the candidate commodity is located and the attribute information in the commodity platform;
determining the same commodity in each candidate commodity according to the attribute information of each candidate commodity;
and comparing and displaying the description information of the same commodity.
An embodiment of a second aspect of the present disclosure provides a processing apparatus of commodity information, including:
the receiving module is used for receiving a commodity information query request, wherein the query request comprises commodity keywords;
the acquisition module is used for inquiring a commodity information database to acquire each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, wherein the commodity information database comprises the attribute information of each commodity in each commodity platform, and one description information of each candidate commodity comprises the commodity platform where the candidate commodity is located and the attribute information in the commodity platform;
the determining module is used for determining the same commodity in each candidate commodity according to the attribute information of each candidate commodity;
and the display module is used for comparing and displaying the description information of the same commodity.
An embodiment of a third aspect of the present disclosure provides a computer device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein when the processor executes the program, the method for processing the commodity information is realized.
A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements a method for processing commodity information as set forth in the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product, which when executed by an instruction processor in the computer program product, performs the method for processing commodity information provided in the first aspect of the present disclosure.
The commodity information processing method, the commodity information processing device, the computer equipment and the storage medium, which are provided by the disclosure, can firstly receive a commodity information query request, then query the commodity information database to obtain each candidate commodity matched with the commodity key words and each description information of each candidate commodity, and then determine the same commodity in each candidate commodity according to the attribute information of each candidate commodity, namely, compare and display each description information of the same commodity. Therefore, by querying the commodity information database, the attribute information of the commodity which the user wants to query in each commodity platform can be determined, and the attribute information of the same commodity can be compared and displayed, so that the manual comparison process is reduced, and the efficiency and the accuracy are improved.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a processing method of commodity information according to an embodiment of the disclosure;
fig. 2A is a schematic flowchart of a processing method of commodity information according to another embodiment of the present disclosure;
FIG. 2B is a schematic diagram of model training provided by another embodiment of the present disclosure;
FIG. 2C is a schematic view of an interface provided by another embodiment of the present disclosure;
fig. 3A is a schematic structural diagram of a device for processing merchandise information according to an embodiment of the disclosure;
fig. 3B is a schematic diagram illustrating a method for processing merchandise information according to another embodiment of the disclosure;
fig. 3C is a schematic diagram of an e-commerce application platform architecture according to another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a device for processing merchandise information according to another embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.
A method, an apparatus, a computer device, and a storage medium for processing commodity information according to an embodiment of the present disclosure are described below with reference to the drawings.
Fig. 1 is a schematic flow chart of a processing method of commodity information according to an embodiment of the present disclosure.
The embodiment of the present disclosure is exemplified in that the method for processing the product information is configured in a product information processing apparatus, and the product information processing apparatus may be applied to any computer device, so that the computer device may execute a product information processing function.
The Computer device may be a Personal Computer (PC), a cloud device, a mobile device, and the like, and the mobile device may be a hardware device having various operating systems, touch screens, and/or display screens, such as a mobile phone, a tablet Computer, a Personal digital assistant, a wearable device, and an in-vehicle device.
For convenience of explanation, the processing device of the product information in the following embodiments of the present disclosure is simply referred to as "processing device".
As shown in fig. 1, the method for processing the commodity information may include the steps of:
In the embodiment of the present disclosure, when a user wants to query commodity information, a commodity information query request may be sent to the processing device, and the processing device may obtain the commodity information query request.
The query request acquired by the processing device may be sent by the user through an application program, or may also be sent by the user through a web page, for example, triggering a specific control in the web page, which is not limited in this disclosure.
In addition, the query request may include a keyword of the commodity, and may also include a keyword of related information that is relatively concerned by the user. For example, if the user wants to query the information of the mobile phone a, the user can directly input "mobile phone a"; or, the user pays attention to the memory and price related information of the mobile phone a, and may also input "mobile phone a, memory, price", and the like, which is not limited by the disclosure.
The commodity information database may include attribute information of each commodity in each commodity platform, and one description information of each candidate commodity may include a commodity platform where the candidate commodity is located and attribute information in the commodity platform.
The commodity information database stores a large number of commodities and corresponding description information in advance.
In addition, the attribute information may include specification, model, price, etc. of the commodity, which is not limited in this disclosure.
It can be understood that, after receiving the product information query request, the processing device may match the product keywords contained therein with the respective products in the product information database to obtain the respective candidate products and the respective description information of each candidate product.
For example, the commodity keywords are: brand B computer. The processing device can be used for respectively matching the brand B computer with each commodity in the commodity information database, and the obtained product is matched with the commodity keyword: the candidate product 1, the corresponding description information is: the product name is computer B1, platform 1, model is AABB, color is white, and price is 3200 yuan; and the candidate commodity 2 corresponds to the description information: the product name is computer B1, platform 2, model is AABB, color is red, and price is 3000 yuan; and the candidate commodity 3 corresponds to the description information: the product name is computer B2, platform 2, model AAA, color red, and price 3500 Yuan.
It should be noted that the above examples are merely illustrative, and are not intended to limit candidate products, various description information, and the like in the embodiments of the present disclosure.
And 103, determining the same goods in each candidate goods according to the attribute information of each candidate goods.
For example, the same product in each candidate product may be determined according to the product name and the model number in each candidate product.
For example, for the candidate product 1, the corresponding description information is: the product is named as computer B1, the model is AABB, and the color is red; and the candidate commodity 2 corresponds to the description information: the product is named as a computer B1, the model is AABB, and the color is white; and the candidate commodity 3 corresponds to the description information: the product name is computer B2, model is AAA, and the color is black. Thus, according to the product name and the model number in each candidate commodity, it can be determined that the candidate commodity 1 and the candidate commodity 2 are the same commodity.
Alternatively, the same product may be specified among the candidate products based on the similarity of the attribute information of the candidate products. For example, a threshold value may be set in advance, and each candidate product having a similarity greater than the threshold value may be determined as the same product.
When determining the similarity of the attribute information of each candidate product, there may be a variety of ways, for example, semantic similarity, cosine similarity, and the like may be used, which is not limited in this disclosure.
For example, the threshold value set in advance is 95%, the similarity between candidate item 1 and candidate item 2 is 0.97, the similarity between candidate item 1 and candidate item 3 is 0.57, and the similarity between candidate item 2 and candidate item 3 is 0.61, so that it can be determined that candidate item 1 and candidate item 2 are the same item.
It should be noted that the above examples are merely illustrative, and are not intended to limit candidate products, similarity degrees, and the like in the embodiments of the present disclosure.
And 104, comparing and displaying the description information of the same commodity.
It will be appreciated that the various descriptors for the same item may be pre-processed. For example, the description information of the same product may be sorted according to a certain rule and then displayed in a contrasting manner.
For example, the same commodity is: candidate item 1 and candidate item 2. Wherein, each description information of the candidate product 1 is: platform 1, product name C3, price 2599, color red, model AABBB, storage capacity 8+128 gigabytes (gigabyte, GB for short); the respective description information of the candidate article 2 is: the product name is C3, the storage capacity is 8+128GB, the model is AABBB, the color is red, the price is 2499, and the platform is 2. The order of the description information of two same commodities has difference, and the description information can be arranged according to the order of platform, product name, price, model, color and storage capacity, and then the comparison display is carried out according to the arranged order.
It should be noted that the above examples are merely illustrative, and are not intended to limit candidate products, similarity degrees, and the like in the embodiments of the present disclosure.
In the embodiment of the present disclosure, the commodity information query request may be received first, and then the commodity information database may be queried to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, and then the same commodity in each candidate commodity is determined according to the attribute information of each candidate commodity, so that each description information of the same commodity may be displayed in a comparison manner. Therefore, by querying the commodity information database, the attribute information of the commodity which the user wants to query in each commodity platform can be determined, and the attribute information of the same commodity can be compared and displayed, so that the manual comparison process is reduced, and the efficiency and the accuracy are improved.
In the embodiment, the attribute information of the commodity which the user wants to query in each commodity platform can be determined by searching in the commodity information database according to the commodity key words, and the attribute information of the same commodity can be compared and displayed. In a possible implementation manner, the attribute information of each candidate product may include an attribute map corresponding to the candidate product and attribute values of each dimension, so that the same product in each candidate product may be determined according to the attribute map corresponding to each candidate product and the attribute values of each dimension, and the process is described in detail below with reference to fig. 2A.
Fig. 2A is a schematic flow chart of a processing method of commodity information according to an embodiment of the present disclosure. As shown in fig. 2A, the processing method of the commodity information may include the following steps:
The product attribute map may include various attribute information of the product, a relationship between various attribute information, and the like, which is not limited in this disclosure.
In the embodiment of the disclosure, the corresponding commodity attribute map can be determined by analyzing and processing the acquired attribute information of each commodity.
In addition, depth-first search (DFS) is a kind of search algorithm, which starts from a certain state, continuously transitions the state until the state cannot transition, then reverts to the state of the previous step, continuously transitions to other states, and repeats this operation until the final solution is found.
For example, according to the food commodity attribute map, depth-first search is sequentially performed on food commodities from each commodity platform, so that attribute information of the food commodities in each commodity platform is completely collected, and then commodity information database information and the like are updated according to the obtained attribute information of each food commodity. The present disclosure is not limited thereto.
In the embodiment of the disclosure, depth-first search can be performed on each commodity platform based on various commodity attribute maps, so that all attribute information of various commodities in each commodity platform can be obtained, the attribute information is more comprehensive and complete, an updated commodity information database is more complete and complete, and meanwhile, a data guarantee is provided for removing irrelevant commodities.
Optionally, in order to ensure timeliness and comprehensiveness of updating the commodity information database, the attribute information of each commodity can be acquired from each commodity platform according to a preset frequency, and then the commodity information database is updated according to the acquired attribute information.
For example, the preset frequency is once per hour, and the processing device may sequentially acquire the attribute information of each commodity from each commodity platform every other hour, or may also acquire the attribute information of each commodity from each commodity platform every other hour, and then update the commodity information database and the like according to the acquired attribute information. The present disclosure is not limited thereto.
Optionally, in order to ensure accuracy and integrity of updating the commodity information database, the attribute information of each commodity type can be obtained from each commodity platform according to the commodity-type-based keyword, and the commodity information database is updated.
The article category keyword may be any keyword related to the article category, such as food, beverage, daily product, study article, and the like, which is not limited in this disclosure.
For example, the product key words include "food product", "beverage product" and "clothing product", so that the processing device can sequentially acquire attribute information of "food product" products, attribute information of "beverage product" products and attribute information of "clothing product" products from each product platform; alternatively, the processing device may simultaneously acquire attribute information of "food type" goods, "attribute information of" beverage type "goods," attribute information of "clothing type" goods, and the like, in sequence from each of the goods platforms. And then updating commodity information database information and the like according to the obtained attribute information of each commodity of the categories. The present disclosure is not limited thereto.
In the embodiment of the disclosure, the attribute information of each category of commodity is acquired according to the category, and meanwhile, the commodity information database can be updated according to the category, so that the information processing efficiency is improved.
It can be understood that the three ways of updating the commodity information database can be used individually, or can be selected from a plurality of combinations for use together according to the needs. For example, the attribute information of each category of commodity can be obtained from each commodity platform according to the preset frequency and based on the category keywords, and then the commodity information database is updated; or updating the commodity information database according to various commodity attribute maps and a preset frequency method, and the like, which is not limited by the disclosure.
Step 203, querying the goods information database to obtain each candidate goods matched with the goods keywords and each description information of each candidate goods.
The commodity information database may include attribute information of each commodity in each commodity platform, and one description information of each candidate commodity may include a commodity platform where the candidate commodity is located and attribute information in the commodity platform.
And 204, determining a first similarity among the candidate commodities according to the attribute maps respectively corresponding to the candidate commodities.
Optionally, the trained graph neural network may be used to determine the feature vectors corresponding to the attribute graphs of the candidate commodities, and then the first similarity between the candidate commodities is determined according to the distance between the feature vectors.
There may be various ways to determine the distance between the feature vectors, such as euclidean distance, cosine distance, etc., which is not limited in this disclosure.
For example, the attribute maps corresponding to the candidate commodities are input to the graph neural network, then the feature vectors corresponding to the attribute maps of the candidate commodities can be output through processing of network layers in the graph neural network, then the distance between the feature vectors can be determined by using the euclidean distance, and then the first similarity between the candidate commodities is determined according to the distance between the feature vectors.
It is understood that the first similarity between each candidate item is in a negative correlation with the distance between each feature vector. That is, the larger the distance between the feature vectors is, the smaller the first similarity between the candidate commodities is, the smaller the distance between the feature vectors is, the larger the first similarity between the candidate commodities is, and the like. Therefore, according to the distance between the feature vectors, the first similarity between the candidate commodities can be represented. The present disclosure is not limited thereto.
The following describes a training process of the graph neural network and the commodity matching model provided by the present disclosure, taking fig. 2B as an example.
As shown in fig. 2B, commodity information is collected from each commodity platform and stored in the commodity information database. Firstly, inputting commodity key words, inquiring a commodity information database according to the commodity key words, and determining each candidate commodity.
In order to reduce the data amount of subsequent processing, each candidate commodity can be initially screened according to commodity keywords, such as information of commodity brands and the like. And then, a first similarity between the candidate commodities can be determined by using the graph neural network, and a second similarity of the candidate commodities can be determined by using the commodity matching model. And determining the same commodity in the candidate commodities according to the first similarity and the second similarity, normalizing the attribute information of the same commodity on different commodity platforms, and comparing and displaying the attribute information of the same commodity.
And then, manually marking the display result, and pushing the display result to the user if the accuracy reaches the standard. If the accuracy rate does not reach the standard, the marked result can be added into the training data, model iteration is continued until the accuracy rate reaches the standard, and the training of the neural network and the commodity matching model is completed.
It should be noted that the above examples are only illustrative and cannot be used as limitations for training the neural network of the graph, the commodity matching model, and the like in the embodiments of the present disclosure.
Optionally, the similarity of each candidate product in each same dimension may be determined, and then the second similarity between each candidate product is determined according to the similarity of each same dimension.
When determining the similarity of each candidate product in each same dimension, there may be multiple ways, for example, an euclidean distance, a cosine distance, a hamming distance, and the like may be used, which is not limited in this disclosure.
For example, the attribute information of the candidate product 1 is: the product name is A1 mobile phone, the price is 2599, the color is red, and the storage capacity is 8+128GB; the attribute information of the candidate article 2 is: the product name is A1 mobile phone, the storage capacity is 8+256GB, the color is white, and the price is 2499. Therefore, the candidate product 1 and the candidate product 2 can be determined, the similarity corresponding to each dimension of price, color and storage capacity can be fused, and then the second similarity between the candidate product 1 and the candidate product 2 can be determined.
It can be understood that there are many ways to fuse the similarity corresponding to each dimension. For example, the similarities corresponding to the dimensions of the candidate products may be added, and the obtained result is the second similarity of the candidate products. Or, a threshold may be set, the similarity smaller than the threshold is removed, and then the similarities corresponding to the remaining dimensions of each candidate commodity are summed, and the obtained result is the second similarity of each candidate commodity.
It should be noted that the foregoing examples are only illustrative, and cannot be used as limitations on the similarity of each same dimension, each second similarity, and the like in the embodiments of the present disclosure.
Alternatively, the second similarity between the candidate commodities may be determined by averaging the titles of the candidate commodities and the word vectors of the keywords of the attribute information.
Or, the trained product matching model may be used, and the attribute information of each candidate product is input into the product matching model, so that the importance degree of each attribute information may be determined, and the second similarity between each candidate product may be determined according to the importance degree of each attribute information.
It should be noted that the above examples are merely illustrative, and cannot be used as limitations on the manner of determining each second similarity degree in the embodiments of the present disclosure.
Optionally, the first similarity and the second similarity between the candidate commodities may be fused to obtain a total similarity corresponding to each candidate commodity, and then the same commodity in each candidate commodity is determined according to the total similarity corresponding to each candidate commodity.
When the first similarity and the second similarity between the candidate commodities are fused, various modes can be provided.
For example, the first similarity and the second similarity of each candidate item may be summed, and the obtained result is the total similarity of each candidate item. Alternatively, the total similarity of the candidate products may be obtained by multiplying the respective similarities by the corresponding weights based on the weights corresponding to the first similarity and the second similarity.
In addition, when the same product in each candidate product is determined based on the total similarity corresponding to each candidate product, there may be a plurality of ways.
For example, a first threshold value may be set in advance, and each candidate item having a total similarity greater than the first threshold value may be determined as the same item. Or, the candidate commodities can be ranked from large to small according to the respective corresponding total similarity, and the first five percent of the candidate commodities can be determined as the same commodity.
The above examples are merely illustrative, and are not intended to limit the manner in which the similarities are fused, the manner in which the same product is identified among the candidate products, and the like in the embodiments of the present disclosure.
And step 207, performing normalization processing on the attribute information of the same commodity in different commodity platforms to obtain the normalized attribute information of the same commodity.
And step 208, comparing and displaying the normalized attribute information of the same commodity and the commodity platform where the same commodity is located.
The same commodity is on different commodity platforms, and the attribute information of the same dimension may be different. Therefore, in the embodiment of the disclosure, in order to make the attribute information during comparison and display clearer and more intuitive, the attribute information of the same commodity in different commodity platforms may be normalized first, and then the normalized attribute information of the same commodity and the commodity platform where the normalized attribute information is located may be compared and displayed.
For example, when describing the color characteristics of the product 1, the attribute information used by the a platform is: the color is red, namely the keyword is 'color'; the attribute information used by the platform B is: the color is red, i.e. the keyword is "color". In order to make the contrast display result clearer, the keywords describing the color features may be unified into "color".
Or, the attribute information may be arranged in the same order, so that the comparison and display of the attribute information may be clearer and more intuitive.
For example, the interface for comparing and displaying the normalized attribute information of the same product and the product platform where the same product is located may be as shown in fig. 2C.
As can be seen from fig. 2C, there are three identical items, located on platform 1, platform 2, and platform 3. The attribute information of three same commodities is displayed in sequence according to the commodity title, brand, model, color, screen size, screen type and the like. Therefore, the user can clearly and visually know the attribute information of the commodity, the cost of manual processing is reduced, and the efficiency is improved.
It should be noted that the above examples are only illustrative, and cannot be used as limitations on specific contents of normalization processing, a contrast display manner, and the like in the embodiments of the present disclosure.
According to the embodiment of the disclosure, the commodity information database can be updated according to the acquired attribute information of various commodities in each commodity platform, after a commodity information query request is received, the commodity information database is queried to acquire each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, then, according to the attribute maps respectively corresponding to the candidate commodities and the attribute values in the same dimension, the first similarity and the second similarity between the candidate commodities are determined, so that the same commodity in each candidate commodity is determined, the attribute information of the same commodity in different commodity platforms can be normalized, and the normalized attribute information of the same commodity and the commodity platform where the same commodity is located are displayed in a comparison manner. Therefore, each candidate commodity can be determined by querying the commodity information database, and then the attribute information of the commodity which the user wants to query in each commodity platform can be determined according to each similarity between the candidate commodities, and the attribute information of the same commodity can be compared and displayed, so that the process of manual comparison is reduced, and the efficiency and the accuracy are improved.
In the embodiment, each candidate commodity can be determined by querying the commodity information database, and further, according to each similarity between the candidate commodities, the attribute information of the commodity which the user wants to query in each commodity platform can be determined, and the attribute information of the same commodity can be displayed in a contrasting manner. In a possible implementation manner, the attribute vector of each candidate commodity in each dimension may be determined according to the attribute value of each candidate commodity in each dimension, and then the second similarity between the candidate commodities is determined, which is described in detail below with reference to fig. 3A.
Fig. 3A is a schematic flowchart of a processing method of commodity information according to an embodiment of the present disclosure. As shown in fig. 3A, the processing method of the commodity information may include the following steps:
The commodity information database may include attribute information of each commodity in each commodity platform, and one description information of each candidate commodity may include a commodity platform where the candidate commodity is located and attribute information in the commodity platform.
A method of determining the first similarity between each candidate commodity will be described below with reference to a schematic diagram shown in fig. 3B.
In the schematic diagram shown in fig. 3B, the attribute maps corresponding to the candidate product 1 and the candidate product 2 may be respectively input into the map neural network. The mapping layer in the graph neural network may map the attribute map corresponding to the candidate commodity 1 input therein to a corresponding vector, the subsequent weighting layer may further determine the vector corresponding to the attribute map and the weight of each vector, and the feature vector of the candidate commodity 1 may be output through a highway layer (highway layers)
Correspondingly, after the attribute map corresponding to the candidate commodity 2 is input into the graph neural network, the feature vector corresponding to the candidate commodity 2 can be output through the layer-by-layer processing of the graph neural network
Then, it can determine
And
d (e) between 1 ，e 2 ) And further according to the distance d (e) 1 ，e 2 ) A first similarity between candidate item 1 and candidate item 2 is determined.
It is to be understood that the graph neural network shown in fig. 3B is only schematic illustration, and it may also include other network layers, or it may be other network structures, and so on. The present disclosure is not limited thereto.
It should be noted that the above examples are only illustrative and should not be taken as a limitation on the neural network, the determination of the first similarity between the candidate commodities, and the like in the embodiment of the present disclosure.
And step 304, determining an attribute vector of each candidate commodity in each dimension according to the attribute value of each candidate commodity in each dimension.
There may be multiple ways to determine the attribute vector of the candidate product in each dimension.
For example, the trained model may be used to map the attribute values of each candidate product in each dimension to a corresponding attribute vector.
For example, the attribute value of each candidate commodity in each dimension may be input into the neural network, and then the attribute vector of each candidate commodity in each dimension may be output through the processing of the neural network.
Alternatively, the attribute value of each candidate product in each dimension may be mapped to an attribute vector of each candidate product in each dimension using a character mapping table or the like.
It should be noted that the above examples are only illustrative, and cannot be taken as a limitation on the manner of determining the attribute vector of each candidate product in each dimension in the embodiments of the present disclosure.
And 305, determining a total description vector corresponding to each candidate commodity according to the attribute vector of each candidate commodity in each dimension.
When determining the total description vector corresponding to each candidate commodity, there may be multiple ways.
For example, the attribute vectors of each candidate product in each dimension may be spliced, and the spliced vector is the total description vector corresponding to each candidate product.
Or, the attribute vectors of each candidate commodity in each dimension may be added first, and then the sum is averaged to obtain the total description vector corresponding to each candidate commodity.
Or, based on the weight corresponding to each dimension, the attribute vector of each candidate product in each dimension is multiplied by the corresponding weight and then added, and the obtained sum is the total description vector corresponding to each candidate product.
It should be noted that the above examples are only illustrative, and cannot be taken as a limitation on the manner of determining the total description vector corresponding to each candidate product in the embodiments of the present disclosure.
And step 306, determining a second similarity between the candidate commodities according to the distances between the total description vectors respectively corresponding to the candidate commodities.
And the distance between the total description vectors corresponding to the candidate commodities respectively can represent the second similarity between the candidate commodities.
It is understood that the second similarity between each candidate item is in a negative correlation with the distance between each overall description vector. That is, the larger the distance between the total description vectors is, the smaller the second similarity between the candidate items is, the smaller the distance between the total description vectors is, the larger the second similarity between the candidate items is, and the like. The present disclosure is not limited thereto.
Optionally, the weight of each dimension attribute value of each candidate commodity may be determined according to the category to which each candidate commodity belongs, and then the second similarity between each candidate commodity is determined according to the weight of each dimension attribute value of each candidate commodity and the attribute value of each candidate commodity in the same dimension.
The candidate goods are different in category, and the weights of the dimension attribute values may be different.
For example, the categories of candidate goods are: the food class may set the weight of the attribute value corresponding to the production date to be larger, and the weight of the attribute values of other dimensions to be smaller. And then multiplying and adding the weight of each dimension attribute value of each candidate commodity and the attribute value of each candidate commodity in the same dimension, wherein the obtained result is the second similarity among the candidate commodities.
The above examples are merely illustrative, and are not intended to limit the categories to which the candidate products belong, the weights of the dimension attribute values, the manner of determining the second similarity between the candidate products, and the like in the implementation of the present disclosure.
And 308, comparing and displaying the description information of the same commodity.
The processing method of the commodity information provided by the disclosure can be applied to any application platform, application software and the like which need to compare the commodity information, and can be used offline or online, and the like, and the disclosure does not limit the application platform, the application software and the like.
For example, the solution provided by the present disclosure is applied to an e-commerce application platform, and the architecture of the platform may be as shown in fig. 3C.
As shown in fig. 3C, details of the commodity information may be collected from each e-commerce channel, and then stored in the commodity information database, and may be synchronized to the XX big data platform at regular time every day, and then synchronized to the search engine by the XX big data platform, so that the power provider application platform obtains details of the commodity.
In addition, the attribute information corresponding to the commodities can be processed to determine the first similarity and the second similarity corresponding to each commodity, the similarity matching result is synchronized to the XX big data platform, and the matching result is synchronized to the search engine by the XX big data platform.
Therefore, after a user submits a commodity information query request on the E-commerce application platform, the search engine can read the query request submitted by the application platform, and then the commodity matching result can be displayed to the user according to the query request, so that the precise comparison of the same commodity on different platforms is realized, the labor cost is reduced, and the efficiency and the accuracy are improved.
According to the embodiment of the disclosure, after receiving a commodity information query request, a commodity information database may be queried to obtain each candidate commodity matched with a commodity keyword and each description information of each candidate commodity, a first similarity between each candidate commodity is determined according to an attribute map corresponding to each candidate commodity, an attribute vector of each candidate commodity in each dimension and a total description vector corresponding to each candidate commodity are determined according to an attribute value of each candidate commodity in each dimension, and a second similarity between each candidate commodity is determined. And then determining the same commodity in each candidate commodity according to the first similarity and the second similarity among each candidate commodity, and then comparing and displaying each description information of the same commodity. Therefore, each candidate commodity can be determined by querying the commodity information database, and then the attribute information of the commodity which the user wants to query in each commodity platform can be determined according to each similarity between the candidate commodities, and the attribute information of the same commodity can be compared and displayed, so that the process of manual comparison is reduced, and the efficiency and the accuracy are improved.
In order to implement the above embodiments, the present disclosure further provides a processing apparatus for commodity information.
Fig. 4 is a schematic structural diagram of a device for processing merchandise information according to an embodiment of the present disclosure.
As shown in fig. 4, the product information processing apparatus 100 may include: a receiving module 110, an obtaining module 120, a determining module 130 and a displaying module 140.
The receiving module 110 is configured to receive a commodity information query request, where the query request includes a commodity keyword.
The obtaining module 120 is configured to query a commodity information database to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, where the commodity information database includes attribute information of each commodity in each commodity platform, and one description information of each candidate commodity includes a commodity platform where the candidate commodity is located and attribute information in the commodity platform.
The determining module 130 is configured to determine the same product in each candidate product according to the attribute information of each candidate product.
The display module 140 is configured to compare and display the description information of the same product.
Optionally, the apparatus may further include an updating module, configured to update the commodity information database according to attribute information of each commodity collected from each commodity platform at a preset frequency.
The updating module is further used for updating the commodity information database according to the attribute information of each commodity type commodity obtained from each commodity platform based on the commodity type keyword.
The updating module is further configured to perform depth-first search on each commodity platform according to the attribute maps of the various commodities, acquire attribute information of the various commodities in each commodity platform, and update the commodity information database.
Optionally, the attribute information of each candidate product includes an attribute map corresponding to the candidate product and attribute values of each dimension, and the determining module includes:
the first determining unit is used for determining a first similarity among the candidate commodities according to the attribute maps corresponding to the candidate commodities respectively;
the second determining unit is used for determining second similarity among the candidate commodities according to the attribute values of the candidate commodities in the same dimension;
and the third determining unit is used for determining the same commodity in the candidate commodities according to the first similarity and the second similarity between the candidate commodities.
Optionally, the second determining unit is specifically configured to:
determining an attribute vector of each candidate commodity in each dimension according to the attribute value of each candidate commodity in each dimension;
determining a total description vector corresponding to each candidate commodity according to the attribute vector of each candidate commodity in each dimension;
and determining a second similarity between the candidate commodities according to the distance between the total description vectors respectively corresponding to the candidate commodities.
Optionally, the second determining unit is further specifically configured to:
determining the weight of each dimension attribute value of each candidate commodity according to the category of each candidate commodity;
and determining second similarity among the candidate commodities according to the weight of the attribute value of each dimension of each candidate commodity and the attribute value of each candidate commodity in the same dimension.
Optionally, the display module is specifically configured to:
normalizing the attribute information of the same commodity in different commodity platforms to obtain the normalized attribute information of the same commodity;
and comparing and displaying the normalized attribute information of the same commodity and the commodity platform where the same commodity is located.
The functions and specific implementation principles of the modules in the embodiments of the present disclosure may refer to the embodiments of the methods, and are not described herein again.
The commodity information processing device according to the embodiment of the present disclosure may receive a commodity information query request, then query the commodity information database to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, and then determine the same commodity in each candidate commodity according to the attribute information of each candidate commodity, that is, may compare and display each description information of the same commodity. Therefore, by inquiring the commodity information database, the attribute information of the commodity which the user wants to inquire in each commodity platform can be determined, and the attribute information of the same commodity can be compared and displayed, so that the manual comparison process is reduced, and the efficiency and the accuracy are improved.
In order to implement the foregoing embodiment, the present disclosure further provides a computer device, including: the present disclosure provides a method for processing commodity information, which is provided by the foregoing embodiments of the present disclosure, when the processor executes a program.
In order to achieve the above embodiments, the present disclosure also proposes a non-transitory computer readable storage medium storing a computer program, which when executed by a processor, implements the processing method of the commodity information as proposed by the foregoing embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure also provides a computer program product, which when executed by an instruction processor in the computer program product, performs the processing method of the merchandise information as provided in the foregoing embodiments of the present disclosure.
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure. The computer device 12 shown in fig. 5 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.
According to the technical scheme, the commodity information query request can be received firstly, then the commodity information database is queried so as to obtain each candidate commodity matched with the commodity key words and each description information of each candidate commodity, then the same commodity in each candidate commodity is determined according to the attribute information of each candidate commodity, and then each description information of the same commodity can be compared and displayed. Therefore, by querying the commodity information database, the attribute information of the commodity which the user wants to query in each commodity platform can be determined, and the attribute information of the same commodity can be compared and displayed, so that the manual comparison process is reduced, and the efficiency and the accuracy are improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (15)
1. A commodity information processing method is characterized by comprising the following steps:
receiving a commodity information query request, wherein the query request comprises commodity keywords;
querying a commodity information database to obtain each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, wherein the commodity information database comprises attribute information of each commodity in each commodity platform, and one description information of each candidate commodity comprises a commodity platform where the candidate commodity is located and the attribute information in the commodity platform;
determining the same commodity in each candidate commodity according to the attribute information of each candidate commodity;
and comparing and displaying the description information of the same commodity.
2. The method of claim 1, further comprising one or more of the following steps:
updating the commodity information database according to the attribute information of each commodity collected from each commodity platform at a preset frequency;
updating the commodity information database according to the attribute information of each commodity type commodity obtained from each commodity platform based on the commodity type key words;
and performing depth-first search on each commodity platform according to the attribute maps based on various commodities, acquiring attribute information of various commodities in each commodity platform, and updating the commodity information database.
3. The method as claimed in claim 1, wherein the attribute information of each candidate product includes an attribute map corresponding to the candidate product and attribute values of respective dimensions, and the determining the same product in the respective candidate products according to the attribute information of the respective candidate products comprises:
determining a first similarity between the candidate commodities according to the attribute maps corresponding to the candidate commodities respectively;
determining a second similarity among the candidate commodities according to the attribute values of the candidate commodities in the same dimension;
and determining the same commodity in each candidate commodity according to the first similarity and the second similarity among the candidate commodities.
4. The method as claimed in claim 3, wherein said determining a second similarity between said candidate commodities according to attribute values of said candidate commodities in the same dimension comprises:
determining an attribute vector of each candidate commodity in each dimension according to the attribute value of each candidate commodity in each dimension;
determining a total description vector corresponding to each candidate commodity according to the attribute vector of each candidate commodity in each dimension;
and determining a second similarity between the candidate commodities according to the distances between the total description vectors respectively corresponding to the candidate commodities.
5. The method as claimed in claim 3, wherein said determining a second similarity between said candidate commodities according to attribute values of said candidate commodities in the same dimension comprises:
determining the weight of each dimension attribute value of each candidate commodity according to the category of each candidate commodity;
and determining a second similarity among the candidate commodities according to the weight of the dimension attribute value of each candidate commodity and the attribute value of each candidate commodity in the same dimension.
6. The method according to any one of claims 1 to 5, wherein the comparative display of the respective description information of the same commodity comprises:
normalizing the attribute information of the same commodity in different commodity platforms to obtain the normalized attribute information of the same commodity;
and comparing and displaying the normalized attribute information of the same commodity and the commodity platform where the same commodity is located.
7. An apparatus for processing commodity information, comprising:
the receiving module is used for receiving a commodity information query request, wherein the query request comprises commodity keywords;
the acquisition module is used for inquiring a commodity information database to acquire each candidate commodity matched with the commodity keyword and each description information of each candidate commodity, wherein the commodity information database comprises the attribute information of each commodity in each commodity platform, and one description information of each candidate commodity comprises the commodity platform where the candidate commodity is located and the attribute information in the commodity platform;
the determining module is used for determining the same commodity in each candidate commodity according to the attribute information of each candidate commodity;
and the display module is used for comparing and displaying the description information of the same commodity.
8. The apparatus of claim 7, further comprising:
the updating module is used for updating the commodity information database according to the attribute information of each commodity collected from each commodity platform at a preset frequency;
the updating module is also used for updating the commodity information database according to the attribute information of each commodity of the categories, which is acquired from each commodity platform based on the category keywords;
the updating module is further configured to perform depth-first search on each commodity platform according to the attribute maps of the various commodities, acquire attribute information of the various commodities in each commodity platform, and update the commodity information database.
9. The apparatus of claim 7, wherein the attribute information of each candidate product comprises an attribute map corresponding to the candidate product and attribute values of respective dimensions, and the determining module comprises:
the first determining unit is used for determining first similarity among the candidate commodities according to the attribute maps corresponding to the candidate commodities respectively;
the second determining unit is used for determining second similarity among the candidate commodities according to the attribute values of the candidate commodities in the same dimension;
and the third determining unit is used for determining the same commodity in each candidate commodity according to the first similarity and the second similarity between each candidate commodity.
10. The apparatus of claim 9, wherein the second determining unit is specifically configured to:
determining an attribute vector of each candidate commodity in each dimension according to the attribute value of each candidate commodity in each dimension;
determining a total description vector corresponding to each candidate commodity according to the attribute vector of each candidate commodity in each dimension;
and determining a second similarity between the candidate commodities according to the distances between the total description vectors respectively corresponding to the candidate commodities.
11. The apparatus of claim 9, wherein the second determining unit is further specifically configured to:
determining the weight of each dimension attribute value of each candidate commodity according to the category to which each candidate commodity belongs;
and determining a second similarity among the candidate commodities according to the weight of the dimension attribute value of each candidate commodity and the attribute value of each candidate commodity in the same dimension.
12. The apparatus according to any one of claims 7-11, wherein the display module is specifically configured to:
normalizing the attribute information of the same commodity in different commodity platforms to obtain the normalized attribute information of the same commodity;
and comparing and displaying the normalized attribute information of the same commodity and the commodity platform where the same commodity is located.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of processing merchandise information according to any one of claims 1-6 when executing the program.
14. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the method for processing the commodity information according to any one of claims 1 to 6.
15. A computer program product, characterized by comprising a computer program which, when executed by a processor, implements a method of processing merchandise information according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110548013.2A CN115375385A (en) | 2021-05-19 | 2021-05-19 | Commodity information processing method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110548013.2A CN115375385A (en) | 2021-05-19 | 2021-05-19 | Commodity information processing method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115375385A true CN115375385A (en) | 2022-11-22 |
Family
ID=84058590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110548013.2A Pending CN115375385A (en) | 2021-05-19 | 2021-05-19 | Commodity information processing method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115375385A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116049741A (en) * | 2023-04-03 | 2023-05-02 | 欧瑞科斯科技产业(集团)有限公司 | Method and device for quickly identifying commodity classification codes, electronic equipment and medium |
| CN116521906A (en) * | 2023-04-28 | 2023-08-01 | 广州商研网络科技有限公司 | Meta description generation method, device, equipment and medium thereof |
-
2021
- 2021-05-19 CN CN202110548013.2A patent/CN115375385A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116049741A (en) * | 2023-04-03 | 2023-05-02 | 欧瑞科斯科技产业(集团)有限公司 | Method and device for quickly identifying commodity classification codes, electronic equipment and medium |
| CN116521906A (en) * | 2023-04-28 | 2023-08-01 | 广州商研网络科技有限公司 | Meta description generation method, device, equipment and medium thereof |
| CN116521906B (en) * | 2023-04-28 | 2023-10-24 | 广州商研网络科技有限公司 | Meta description generation method, device, equipment and medium thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Van Eck et al. | Software survey: VOSviewer, a computer program for bibliometric mapping | |
| CN110543592B (en) | Information searching method and device and computer equipment | |
| US7529740B2 (en) | Method and apparatus for organizing data sources | |
| US20170177733A1 (en) | Tenantization of search result ranking | |
| US10438133B2 (en) | Spend data enrichment and classification | |
| CN107111640B (en) | Method and user interface for presenting auxiliary content with image search results | |
| US8285721B2 (en) | Mapping item records to product records | |
| KR20190119679A (en) | Systems and methods to seed a search | |
| CN115375385A (en) | Commodity information processing method and device, computer equipment and storage medium | |
| CN112597182A (en) | Data query statement optimization method and device, terminal and storage medium | |
| CN111310065A (en) | Social contact recommendation method and device, server and storage medium | |
| US8577814B1 (en) | System and method for genetic creation of a rule set for duplicate detection | |
| CN111754300A (en) | Commodity recommendation method, commodity recommendation device, commodity recommendation equipment and storage medium | |
| CN111445302A (en) | Commodity sorting method, system and device | |
| US20130138480A1 (en) | Method and apparatus for exploring and selecting data sources | |
| CN113343105A (en) | Keyword page management system, method, equipment and computer readable storage medium | |
| Lu et al. | Clustering e-commerce search engines based on their search interface pages using WISE-Cluster | |
| Mehrbod et al. | Evaluation of an E-catalogue matching mechanism in public procurement notice search | |
| CN110489414A (en) | Report method for pushing and equipment | |
| CN111930967B (en) | Data query method and device based on knowledge graph and storage medium | |
| Scuturici et al. | Topological representation model for image database query | |
| US20230196181A1 (en) | Intelligent machine-learning model catalog | |
| CN112561657B (en) | Vehicle information processing method and device, electronic equipment and storage medium | |
| CN113837846B (en) | Commodity recommendation method, commodity recommendation device, computer equipment and storage medium | |
| WO2022130635A1 (en) | Graph display device, graph display method, and graph display program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |