CN111316306A - Intelligent terminal-based commodity recommendation method and commodity recommendation system - Google Patents
Intelligent terminal-based commodity recommendation method and commodity recommendation system Download PDFInfo
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- CN111316306A CN111316306A CN201780096621.6A CN201780096621A CN111316306A CN 111316306 A CN111316306 A CN 111316306A CN 201780096621 A CN201780096621 A CN 201780096621A CN 111316306 A CN111316306 A CN 111316306A
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Abstract
The invention provides a commodity recommendation method and a commodity recommendation system based on an intelligent terminal. The commodity recommendation method comprises the following steps: receiving a name to be purchased of a commodity to be purchased; judging the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity; matching a corresponding preset commodity class for the to-be-purchased class in a preset catalog; and recommending a preset commodity list associated with the preset commodity category. The commodity recommendation system includes: the device comprises a receiving module, a judging module, a matching module and a recommending module. The invention performs objective analysis on the commodities based on big data, provides an optimized purchasing scheme for the user, can ensure that the user purchases high-quality products, can save purchasing time for the user, and brings a better online purchasing experience for the user.
Description
The invention relates to the field of control of intelligent terminals, in particular to a commodity recommendation method and a commodity recommendation system based on an intelligent terminal.
At present, the rapid development of internet technology changes the current life style of people, for example: taxi, payment, shopping, ticket booking, dining, etc. Due to the fact that the internet is universal, even if a user can not go home and lightly click the mouse, products all over the world can be immediately purchased.
At present, in order to facilitate the selection of users, the online shopping mall generally adopts different recommendation rules to recommend commodities for the users, including: purchase quantity, quality, price, correlation degree and other recommendation factors. However, since the ordering of stores or commodities can be improved by a purchasing mechanism based on the online shopping mall, the purchasing data of a single online shopping mall often cannot objectively represent the quality of a certain commodity.
Therefore, a commodity recommendation method and a commodity recommendation system based on an intelligent terminal are needed to be provided, which can provide an optimized purchase scheme for a user by distinguishing commodity types based on big data of commodities purchased by the user on different network platforms, setting a comprehensively ordered commodity list for each commodity type association and performing objective analysis on the commodities based on the big data, thereby ensuring that the user can purchase high-quality products, saving purchase time for the user and bringing a better online purchase experience for the user.
Disclosure of Invention
In order to solve the problems, the invention provides a commodity recommendation method and a commodity recommendation system based on an intelligent terminal. The method can be used for providing an optimized purchasing scheme for the user by distinguishing the categories of the commodities based on the big data of the commodities purchased by the user on different network platforms, setting a comprehensively ordered commodity list for the association of each category of the commodities and carrying out objective analysis on the commodities based on the big data, so that the user can be ensured to purchase high-quality products, the purchasing time can be saved for the user, and a better online purchasing experience is brought for the user.
The invention provides a commodity recommendation method based on an intelligent terminal, which comprises the following steps:
receiving a name to be purchased of a commodity to be purchased;
judging the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity;
matching a corresponding preset commodity class for the to-be-purchased class in a preset catalog;
and recommending a preset commodity list associated with the preset commodity category.
Preferably, the commodity recommendation method further includes:
recording information data of purchased commodities of each user;
classifying each purchased commodity to establish a preset catalog containing the preset commodity classes;
and associating a preset commodity list for each preset commodity category according to the information data.
Preferably, according to the information data, the step of associating a preset commodity list with each preset commodity category further includes:
reading the information data to obtain ranking factors including a purchased name, a purchased quantity, a purchased category, a purchased price and/or a purchased brand of the purchased commodity;
sorting the purchased commodities under each purchased commodity category according to the sorting factor;
and storing the purchased commodities which are ranked into the top 10 in the preset commodity list which is associated with the preset commodity class which is the same as the purchased commodity class.
Preferably, the commodity recommendation method further includes:
and updating the preset commodity list in a preset time period.
Preferably, the step of sorting the purchased goods under each purchased category according to the purchased names, the purchased quantities, the purchased prices and the purchased brands further comprises:
identifying a calculation condition instruction;
according to the calculation condition instruction, ordering the purchased commodities under each purchased commodity class;
wherein the calculating a conditional instruction comprises increasing the ordering factor, decreasing the ordering factor, or changing the ordering factor.
The invention further provides a commodity recommendation system based on the intelligent terminal, and the commodity recommendation system comprises: the device comprises a receiving module, a judging module, a matching module and a recommending module;
the receiving module is in communication connection with the judging module and receives a name to be purchased of a commodity to be purchased;
the judging module is in communication connection with the receiving module and the matching module and judges the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity;
the matching module is in communication connection with the judging module and the recommending module, and matches a corresponding preset commodity class for the to-be-purchased commodity class in a preset catalog;
the recommending module is in communication connection with the matching module and recommends a preset commodity list associated with the preset commodity category.
Preferably, the goods recommendation system further comprises: the system comprises a recording module, an establishing module and a correlation module;
the recording module is used for recording the information data of the purchased commodities of each user;
the establishing module is in communication connection with the recording module and the matching module and classifies each purchased commodity to establish a preset catalog containing preset commodity categories;
the association module is in communication connection with the recording module, the establishing module and the recommending module, and associates a preset commodity list with each preset commodity category according to the information data.
Preferably, the association module further comprises:
a reading unit which reads the information data to obtain a ranking factor including a purchased name, a purchased quantity, a purchased category, a purchased price and/or a purchased brand of the purchased commodity;
the ordering unit is used for ordering the purchased commodities under each purchased commodity type according to the ordering factors;
the storage unit stores the purchased commodities ranked in the top 10 digits in the preset commodity list associated with the preset commodity class which is the same as the purchased commodities.
Preferably, the commodity recommendation system further comprises an update module;
and the updating module is in communication connection with the association module and updates the preset commodity list within a preset time period.
Preferably, the sorting unit further comprises:
an identification element that identifies a calculation condition instruction;
the execution element is used for sequencing the purchased commodities under each purchased commodity class according to the calculation condition instruction;
wherein the calculating a conditional instruction comprises increasing the ordering factor, decreasing the ordering factor, or changing the ordering factor.
Compared with the prior art, the invention has the technical advantages that:
the method can be used for providing an optimized purchasing scheme for the user by distinguishing the categories of the commodities based on the big data of the commodities purchased by the user on different network platforms, setting a comprehensively ordered commodity list for the association of each category of the commodities and carrying out objective analysis on the commodities based on the big data, so that the user can be ensured to purchase high-quality products, the purchasing time can be saved for the user, and a better online purchasing experience is brought for the user.
Fig. 1 is a schematic flow chart of a method for recommending a commodity based on an intelligent terminal according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for recommending a commodity based on an intelligent terminal according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a commodity recommendation system based on an intelligent terminal according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the present invention.
The advantages of the invention are explained in detail below with reference to the drawings and the embodiments.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Fig. 1 is a schematic flow chart of a commodity recommendation method based on an intelligent terminal according to an embodiment of the present invention. As can be seen from the figure, in this embodiment, the commodity recommendation method includes the following steps:
-receiving a name to be purchased of a commodity to be purchased;
when a user needs to purchase a commodity on a certain network platform through an intelligent terminal, in order to obtain a more convenient and high-quality shopping experience, the user can firstly start a commodity recommendation program in the intelligent terminal so as to enter a commodity recommendation mode. When the intelligent terminal is in the commodity recommendation mode, the user can input the name of the commodity to be purchased in a search box, for example, the name of the commodity to be purchased is input as a.
-determining a to-be-purchased item class of the to-be-purchased item according to the to-be-purchased name of the to-be-purchased item;
after the name to be purchased is input, the intelligent terminal extracts the field in the name to be purchased and obtains the class to be purchased to which the commodity to be purchased of the user belongs by matching the field with the remote database. Wherein the categories include major categories and minor categories. For example, when the name of the product to be purchased is "skirt", the smart terminal obtains the major category of the product to be purchased as dress and the minor category of the product to be purchased as dress or lady dress according to the field matching.
-matching a corresponding predetermined commodity class for the class to be purchased in a predetermined catalog;
after the type of the commodity to be purchased by the user is judged and obtained, the intelligent terminal can automatically call one preset catalog. In the preset catalog, a preset commodity class is recorded. And the intelligent terminal automatically matches the corresponding preset commodity class in the preset catalogue according to the commodity class to be purchased obtained by judgment, and preferably matches the commodity subclass so as to provide more accurate commodity recommendation for the user. For example, if the major category of the merchandise to be purchased is dress and the minor category is skirt, the predetermined merchandise category corresponding to the skirt is preferentially matched. If no skirt exists in the preset commodity categories, the preset commodity categories corresponding to the clothes are further matched.
-recommending a preset list of items associated with said preset category of items.
After the matching of the preset commodity categories is completed for the commodities to be purchased by the user, the intelligent terminal automatically recommends a preset commodity list which is set in association with the preset commodity categories to the user. The preset commodity list comprises a commodity recommendation list based on the online shopping big data of the Internet user, and provides objective commodity recommendation and reference for the user to screen commodities to be purchased.
Fig. 2 is a schematic flow chart of a commodity recommendation method based on an intelligent terminal according to an embodiment of the present invention. As shown in the figure, in a preferred embodiment, the merchandise recommendation method further includes:
-recording information data of purchased goods for each user;
in order to objectively and accurately optimize, screen and recommend commodities to be purchased for a user, the intelligent terminal records information data of each commodity purchased on the online shopping mall platform through the internet through the third-party remote server. The information data may include: platform of purchase, item purchased, category, price, origin, quality, quantity, brand, etc. The detailed information data can ensure the reliability and objectivity of the commodity recommendation.
-classifying each of said purchased articles to create a predetermined catalogue containing said predetermined categories of articles;
based on the big data that the user has purchased commodity, intelligent terminal can be automatic classify according to the article to purchase commodity to establish the preset catalogue that contains preset commodity article class, in presetting the catalogue, can obtain the data information of different commodities under each article class through consulting different preset commodity article classes, including the platform of purchasing, the article of purchase, article class, the price, the place of production, the quality, quantity, the brand etc. the intelligent terminal of being convenient for can recommend commodity to the user fast.
-associating a preset merchandise list for each of said preset merchandise categories according to said information data.
Based on the information data of the purchased commodities obtained by the intelligent terminal through big data, the intelligent terminal can sort different products under the same category according to preset rules, preferentially selects the commodities with the front sorting, adds the commodities to a preset commodity list, and sets the preset commodity list and the preset commodity category in a correlation mode, so that once the intelligent terminal locks the preset commodity category according to the field to be purchased input by the user, the preset commodity list can be directly called for the user to refer.
In a preferred embodiment, the step of associating a preset commodity list with each of the preset commodity categories according to the information data further includes:
-reading said information data to obtain ranking factors including a purchased name, a purchased quantity, a purchased category, a purchased price and/or a purchased brand of said purchased goods;
after the information data purchased by the user is obtained, the intelligent terminal automatically reads the information data, extracts the main information such as the name, the quantity, the type, the price and/or the brand of the purchased commodity from the information data, and uses the main information as an evaluation factor for ordering different commodities under the same commodity type.
-sorting the purchased items under each of the purchased categories according to the sorting factor;
according to the ranking factors such as the names, the quantity, the categories, the prices and/or the brands of the purchased commodities, the intelligent terminal can obtain the ranking of the recommendation degrees of different commodities under the same commodity category from high to low based on the comprehensive calculation of the ranking factors.
-saving the purchased items ranked in the top 10 digits in the preset item list associated with the preset item class identical to the purchased item class.
After finishing the sorting of different commodities under the same commodity class, the intelligent terminal can extract the purchased commodities which are 10 digits before the sorting, add the purchased commodities into a preset commodity list, store the purchased commodities in the preset commodity list, and associate the preset commodity list with the preset commodity class, so that when the intelligent terminal finds the matched preset commodity class according to the commodity class to be purchased, the intelligent terminal can automatically send out the preset commodity list to the user, recommends the commodities for the user based on big data, and provides a purchase suggestion and reference for the user.
In a preferred embodiment, the article recommendation method further includes:
-updating said preset list of items within a preset time period.
In order to ensure the accuracy of the big data and provide the most reliable commodity recommendation for the user, the preset commodity list in the intelligent terminal is automatically updated within a preset time period, namely the sequencing of different commodities in the same commodity category is continuously updated through the change of the big data of the commodities purchased by the internet user, so that the latest preset commodity list is obtained, and a more optimized purchasing reference is provided for the user.
In a preferred embodiment, the step of sorting the purchased goods under each purchased category according to the purchased name, the purchased quantity, the purchased price and the purchased brand further comprises:
-identifying a computation conditional instruction;
in order to ensure that the preset commodity list provided by the intelligent terminal is more suitable for the purchase demand of the user, the user can further adjust the sorting factors of different commodities under the same commodity class by sending a calculation condition instruction to the intelligent terminal.
-ordering said purchased items under each of said purchased categories according to said calculation conditional instructions;
according to the calculation condition instruction actually sent by the user to the intelligent terminal, the intelligent terminal can further sort different commodities under the preset commodity category according to the calculation condition so as to obtain a preset commodity list more conforming to the actual screening condition of the user. For example, if the user pays more attention to the price of the purchased goods, a calculation condition instruction about the price can be sent to the intelligent terminal, and the sorting in the preset goods list finally provided by the intelligent terminal mainly sorts the different goods in the goods category according to the price of the purchased goods of the previous user.
-wherein said calculating a conditional instruction comprises increasing said ranking factor, decreasing said ranking factor or changing said ranking factor.
The calculation condition command issued by the user is mainly the adjustment of the ranking factors including the purchased name, the purchased quantity, the purchased price, the purchased brand and the like. For example, when the user purchases the product, only the price is paid attention to, and the preset product list recommended by the intelligent terminal may be only the top 10 recommended products obtained by sorting according to the purchase price.
Fig. 3 is a schematic structural diagram of a commodity recommendation system based on an intelligent terminal according to an embodiment of the present invention. As shown in the drawing, in the present embodiment, the product recommendation system includes: the device comprises a receiving module, a judging module, a matching module and a recommending module; the receiving module is in communication connection with the judging module and receives a name to be purchased of a commodity to be purchased;
when a user needs to purchase a commodity on a certain network platform through an intelligent terminal, in order to obtain a more convenient and high-quality shopping experience, the user can firstly start a commodity recommendation system based on the intelligent terminal so as to enter a commodity recommendation mode. When in the goods recommending mode, the user can input the name of the goods to be purchased in a search box, for example, input the name a to be purchased.
The judging module is in communication connection with the receiving module and the matching module, and judges the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity;
after the receiving module receives the name to be purchased input by the user, the judging module in the commodity recommending system extracts the field in the name to be purchased and obtains the class to be purchased to which the commodity to be purchased by the user belongs by matching the field with the remote database. Wherein the categories include major categories and minor categories. For example, when the name of the product to be purchased is "skirt", the smart terminal obtains the major category of the product to be purchased as dress and the minor category of the product to be purchased as dress or lady dress according to the field matching.
The matching module is in communication connection with the judging module and the recommending module, and matches a corresponding preset commodity class for the to-be-purchased commodity class in a preset catalog;
after the judging module judges and obtains the categories of the commodities which the user wants to purchase, the matching module can automatically call one preset catalog. In the preset catalog, a preset commodity class is recorded. The matching module automatically matches the corresponding preset commodity class in the preset catalogue according to the commodity class to be purchased judged by the judging module, and preferably matches the commodity subclass to provide more accurate commodity recommendation for the user. For example, if the determining module determines that the major category of the goods to be purchased is clothing and the minor category is skirt, the matching module preferentially matches the preset goods category corresponding to the skirt. And if the matching module judges that no skirt exists in the preset commodity categories, further matching the preset commodity categories corresponding to the clothing.
The recommending module is in communication connection with the matching module and recommends a preset commodity list associated with the preset commodity category.
After a matching module of the commodity recommendation system completes the matching of the preset commodity category for the commodity to be purchased by the user, the recommendation module automatically recommends a preset commodity list which is set in association with the preset commodity category to the user. The preset commodity list comprises a commodity recommendation list based on the online shopping big data of the Internet user, and provides objective commodity recommendation and reference for the user to screen commodities to be purchased.
Fig. 4 is a schematic structural diagram of a product recommendation system based on an intelligent terminal according to an embodiment of the present invention. As shown, in a preferred embodiment, the merchandise recommendation system further comprises: the system comprises a recording module, an establishing module and a correlation module; -the recording module recording information data of purchased goods of each user;
in order to objectively and accurately optimize, screen and recommend commodities to be purchased for a user, the commodity recommendation system further comprises a recording module which records information data of each commodity purchased on the online shopping mall platform through the internet through a third-party remote server. The information data may include: platform of purchase, item purchased, category, price, origin, quality, quantity, brand, etc. The detailed information data can ensure the reliability and objectivity of the commodity recommendation.
-the establishing module, in communication with the recording module and the matching module, classifies each purchased commodity to establish a preset catalog containing the preset commodity categories;
based on the big data of the purchased commodities of the user, the establishment module of the commodity recommendation system can automatically classify the purchased commodities according to categories so as to establish a preset catalog containing preset commodity categories, and in the preset catalog, data information of different commodities under each category can be obtained by looking up different preset commodity categories, wherein the data information comprises a purchased platform, purchased articles, categories, prices, places of production, quality, quantity, brands and the like, and the commodity recommendation system can rapidly recommend commodities to the user.
The association module is in communication connection with the recording module, the establishing module and the recommending module, and associates a preset commodity list for each preset commodity category according to the information data.
Based on the information data of the purchased commodities obtained by the recording module through the big data, the association module can sort different products under the same category according to a preset rule, preferentially selects the commodities with the top sorting, adds the commodities to a preset commodity list, and associates the preset commodity list with the preset commodity category, so that once the matching module locks the preset commodity category in the establishment module according to the field to be purchased input by the user, the recommendation module can directly call the preset commodity list in the association module for the reference of the user.
In a preferred embodiment, the association module further comprises: -a reading unit for reading the information data to obtain ranking factors including a purchased name, a purchased amount, a purchased category, a purchased price and/or a purchased brand of the purchased goods;
when the recording module obtains the information data purchased by the user, the reading unit in the association module automatically reads the information data, and extracts the main information such as the name, the quantity, the category, the price and/or the brand of the purchased commodity from the information data as an evaluation factor for ordering different commodities under the same commodity category.
-a sorting unit for sorting the purchased items under each of the purchased categories according to the sorting factor;
according to the sequencing factors such as the names, the number, the categories, the prices and/or the brands of the purchased commodities read by the reading unit, the sequencing unit in the association module can obtain the sequencing of the recommendation degrees of different commodities under the same commodity category from high to low based on the comprehensive calculation of each sequencing factor.
-a saving unit for saving the purchased goods ranked in the top 10 digits in the preset goods list associated with the preset goods category which is the same as the purchased goods category.
After the sorting unit finishes sorting among different commodities under the same commodity category, the storage unit can extract the purchased commodities of the top 10 in the sorting, add the purchased commodities into the preset commodity list, store the purchased commodities and associate the preset commodity list with the preset commodity category, so that when the matching module finds the matched preset commodity category according to the to-be-purchased commodity category, the recommending module can automatically send out a preset commodity list to a user through the associating module, and recommends the commodities for the user based on big data to provide purchasing suggestions and references for the user.
In a preferred embodiment, the merchandise recommendation system further comprises an update module; the updating module is in communication connection with the associating module and updates the preset commodity list within a preset time period.
In order to ensure the accuracy of the big data and provide the most reliable commodity recommendation for the user, the commodity recommendation system is further provided with an updating module, and the preset commodity list in the association module is ensured to be automatically updated within a preset time period, namely, the sequence among different commodities in the same commodity category is continuously updated through the change of the big data of the commodities purchased by the internet user, so that the latest preset commodity list is obtained, and a more optimized purchasing reference is provided for the user.
In a preferred embodiment, the sorting unit further comprises: further comprising:
-an identification element identifying a calculation condition instruction;
in order to ensure that the preset commodity list provided by the sorting unit is more suitable for the purchase demand of the user, the built-in identification element can further identify that the user sends a calculation condition instruction, and further adjusts the sorting factors of different commodities under the same commodity class based on the calculation condition instruction.
-an execution element for ordering said purchased items under each of said purchased categories according to said calculation conditional instructions;
according to the calculation condition instruction which is obtained by the recognition of the recognition element and is actually sent by the user, the execution element can further sort the different commodities under the preset commodity category according to the calculation condition so as to obtain a preset commodity list which is more in line with the actual screening condition of the user. For example, if the user pays more attention to the price of the purchased goods, a calculation condition instruction about the price can be sent to the intelligent terminal, and the sorting in the preset goods list finally provided by the intelligent terminal mainly sorts the different goods in the goods category according to the price of the purchased goods of the previous user.
-wherein said calculating a conditional instruction comprises increasing said ranking factor, decreasing said ranking factor or changing said ranking factor.
The calculation condition command issued by the user is mainly the adjustment of the ranking factors including the purchased name, the purchased quantity, the purchased price, the purchased brand and the like. For example, when the user purchases the product, only the price is paid attention to, and the preset product list recommended by the intelligent terminal may be only the top 10 recommended products obtained by sorting according to the purchase price.
The commodity recommendation method and the commodity recommendation system based on the intelligent terminal can provide an optimized purchase scheme for a user by distinguishing the categories of commodities based on the big data of the commodities purchased by the user on different network platforms, setting a comprehensively ordered commodity list for each commodity category in an associated manner and objectively analyzing the commodities based on the big data, thereby ensuring that the user purchases high-quality products, saving the purchase time for the user and bringing better online purchase experience for the user.
It should be noted that the embodiments of the present invention have been described in terms of preferred embodiments, and not by way of limitation, and that those skilled in the art can make modifications and variations of the embodiments described above without departing from the spirit of the invention.
Claims (10)
- A commodity recommendation method based on an intelligent terminal is characterized in that,the commodity recommendation method comprises the following steps:receiving a name to be purchased of a commodity to be purchased;judging the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity;matching a corresponding preset commodity class for the to-be-purchased class in a preset catalog;and recommending a preset commodity list associated with the preset commodity category.
- The article recommendation method according to claim 1,the commodity recommendation method further comprises the following steps:recording information data of purchased commodities of each user;classifying each purchased commodity to establish a preset catalog containing the preset commodity classes;and associating a preset commodity list for each preset commodity category according to the information data.
- The article recommendation method according to claim 2,according to the information data, in the step of associating a preset commodity list to each preset commodity category, the method further comprises the following steps:reading the information data to obtain ranking factors including a purchased name, a purchased quantity, a purchased category, a purchased price and/or a purchased brand of the purchased commodity;sorting the purchased commodities under each purchased commodity category according to the sorting factor;and storing the purchased commodities which are ranked into the top 10 in the preset commodity list which is associated with the preset commodity class which is the same as the purchased commodity class.
- The article recommendation method according to claim 2 or 3,the commodity recommendation method further comprises the following steps:and updating the preset commodity list in a preset time period.
- The article recommendation method according to claim 3,the step of sorting the purchased goods under each purchased category according to the purchased name, the purchased quantity, the purchased price and the purchased brand further comprises:identifying a calculation condition instruction;according to the calculation condition instruction, ordering the purchased commodities under each purchased commodity class;wherein the calculating a conditional instruction comprises increasing the ordering factor, decreasing the ordering factor, or changing the ordering factor.
- A commodity recommendation system based on an intelligent terminal is characterized in that,the commodity recommendation system includes: the device comprises a receiving module, a judging module, a matching module and a recommending module;the receiving module is in communication connection with the judging module and receives a name to be purchased of a commodity to be purchased;the judging module is in communication connection with the receiving module and the matching module and judges the to-be-purchased item of the to-be-purchased commodity according to the to-be-purchased name of the to-be-purchased commodity;the matching module is in communication connection with the judging module and the recommending module, and matches a corresponding preset commodity class for the to-be-purchased commodity class in a preset catalog;the recommending module is in communication connection with the matching module and recommends a preset commodity list associated with the preset commodity category.
- The merchandise recommendation system of claim 6,the commodity recommending system further includes: the system comprises a recording module, an establishing module and a correlation module;the recording module is used for recording the information data of the purchased commodities of each user;the establishing module is in communication connection with the recording module and the matching module and classifies each purchased commodity to establish a preset catalog containing preset commodity categories;the association module is in communication connection with the recording module, the establishing module and the recommending module, and associates a preset commodity list with each preset commodity category according to the information data.
- The merchandise recommendation system of claim 6,the association module further comprises:a reading unit which reads the information data to obtain a ranking factor including a purchased name, a purchased quantity, a purchased category, a purchased price and/or a purchased brand of the purchased commodity;the ordering unit is used for ordering the purchased commodities under each purchased commodity type according to the ordering factors;the storage unit stores the purchased commodities ranked in the top 10 digits in the preset commodity list associated with the preset commodity class which is the same as the purchased commodities.
- The article recommendation system according to claim 7 or 8,the commodity recommending system further comprises an updating module;and the updating module is in communication connection with the association module and updates the preset commodity list within a preset time period.
- The merchandise recommendation system of claim 8,the sorting unit further includes:an identification element that identifies a calculation condition instruction;the execution element is used for sequencing the purchased commodities under each purchased commodity class according to the calculation condition instruction;wherein the calculating a conditional instruction comprises increasing the ordering factor, decreasing the ordering factor, or changing the ordering factor.
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| CN113095876A (en) * | 2021-04-09 | 2021-07-09 | 青岛海信智慧生活科技股份有限公司 | Commodity recommendation method and device |
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