CN111709813B - Commodity recommendation method based on big data line - Google Patents

Commodity recommendation method based on big data line Download PDF

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CN111709813B
CN111709813B CN202010568599.4A CN202010568599A CN111709813B CN 111709813 B CN111709813 B CN 111709813B CN 202010568599 A CN202010568599 A CN 202010568599A CN 111709813 B CN111709813 B CN 111709813B
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樊馨
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Guangdong Guangdong Marketing Group Co.,Ltd.
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Abstract

The application discloses a commodity recommendation method based on a big data line, which comprises the following steps: the cloud server activates a monitoring thread, monitors prices of a plurality of commodities and acquires discount information of the commodities; judging whether the discount degree of the commodity is higher than a preset threshold value or not; if the value is higher than the preset threshold value, judging whether the commodity is a to-be-recommended commodity; if the commodity is a to-be-recommended commodity, calculating the commodity recommendation index based on the big data line; and generating commodity recommendation information according to the recommendation index, and sending the commodity recommendation information to a user side so that the user side purchases commodities according to the discount information and the recommendation index.

Description

Commodity recommendation method based on big data line
Technical Field
The application relates to the field of electronic commerce, in particular to a commodity recommendation method based on a big data line.
Background
In the field of electronic commerce, most consumers like and are accustomed to making purchases of goods online, which is based on a sophisticated credit and payment system.
However, in the current commodity purchasing process, a user needs to open a specific shopping platform website to carefully select and purchase commodities from a large number of commodities, and the specific price and the specific quality are usually needed, so that the time cost of online shopping is high.
However, the number of discounted sales promotion commodities is large, and a user cannot obtain discount sales promotion information of a desired commodity at the first time, and there is no way to monitor the commodities.
Disclosure of Invention
The embodiment of the application provides a method for recommending commodities on a big data line, which is used for solving the problems of low commodity recommendation optimization rate and simplification in the prior art.
The embodiment of the invention provides a commodity recommendation method based on a big data line, which comprises the following steps:
the cloud server activates a monitoring thread, monitors prices of a plurality of commodities and acquires discount information of the commodities;
judging whether the discount degree of the commodity is higher than a preset threshold value or not;
if the value is higher than the preset threshold value, judging whether the commodity is a to-be-recommended commodity;
if the commodity is a to-be-recommended commodity, calculating the commodity recommendation index based on the big data line;
and generating commodity recommendation information according to the recommendation index, and sending the commodity recommendation information to a user side so that the user side purchases commodities according to the discount information and the recommendation index.
Optionally, the commodity recommendation index based on the big data line is x and meets the requirement
Figure BDA0002548493380000021
Figure BDA0002548493380000022
Wherein, a is the purchasing behavior index of the user, b is the weight proportion, c is the correction parameter, d is the price factor, and f (e) is the product combination logic function.
Optionally, the determining whether the item is a to-be-recommended item includes:
recording the purchasing behavior of the user, and constructing a user portrait based on the purchasing record;
and based on the user portrait, adopting a recommendation algorithm based on an association rule to screen commodities, and screening the commodities to be recommended from a plurality of commodities.
Optionally, the obtaining discount information of the product includes:
acquiring initial discount information of a commodity;
if the user account number has a plurality of coupons, matching the coupons with the commodities one by one;
if the matching is successful, calculating the final discount price of the successfully matched commodity, wherein the final discount price is obtained by accumulating the initial discount of the commodity and the discount of the coupon.
Optionally, before activating the monitoring thread, the method further includes:
estimating the price of the commodity in a specific period based on the historical change curve of the commodity price;
judging whether the discount degree of the estimated price is higher than the preset threshold value or not;
and if the threshold value is higher than the preset threshold value, activating a monitoring thread.
Optionally, the item to be recommended is a combination of items purchased by the user.
According to the method for recommending the commodities on the big data line, the price is monitored by setting the monitoring program, the priority of discount commodity recommendation is determined based on the recommendation index, and the discount commodity recommendation is recommended to the user for purchase, so that the commodity recommendation efficiency is optimized, the commodity recommendation success rate is improved, and the user experience is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below.
Fig. 1 is a schematic flow chart of commodity recommendation in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Fig. 1 provides a flow chart of a commodity recommendation method based on a big data line, and the method comprises the following steps:
s101, activating a monitoring thread by a cloud server, monitoring prices of a plurality of commodities, and acquiring discount information of the commodities;
the cloud server is located in a core layer of a network architecture and is a core unit responsible for adding, deleting and tracking commodity information, inquiring, paying and maintaining information by a user. In the embodiment of the invention, the cloud server is responsible for monitoring massive commodities for the user, and excavating the commodities which are matched with the user and have high price discount strength through a big data mining technology and recommending the commodities to the user.
In the embodiment of the invention, the types of the mass commodities can be various, such as consumer electronics products, household products and the like, and the commodity information can be captured on a specific website and summarized into commodities and a price list. The commodity types and information are various and complicated, the commodity information processed by the cloud server in unit time is large, the content is large, the commodity types can be screened based on the points of interest (POI) of the user, commodities irrelevant to the user are filtered, commodities interested by the user are reserved, and a commodity price list is planned.
Optionally, before the monitoring thread is activated, the cloud server may also estimate the price of the commodity in a specific period based on a historical change curve of the commodity price; after estimation, judging whether the discount degree of the estimated price is higher than a preset threshold value or not; and if the threshold value is higher than the preset threshold value, activating the monitoring thread. Optionally, the cloud server may collect commodity prices in a historical period based on an artificial intelligence algorithm, and train the commodity prices as training samples, so as to estimate the discount strength of the prices at a certain specific time point. For example, the e-commerce platform may perform a sales promotion of a certain commodity in a fixed time period of a year, or may perform a discount sales promotion of all commodities in a certain day, and the price curve of the commodity may change due to the influence of raw material supply and the maturity of technology development, so that based on the historical prices, a neural network algorithm may be used to perform price prediction to calculate the predicted price on a specific date.
The preset threshold may be a discount rate (price reduction rate) or a discount interval, for example, the original price is 100 yuan, the current price is 80 yuan, the discount rate is 0.2, and the discount interval is 20. And the discount degree corresponds to a preset threshold, and if the discount degree is greater than the discount rate of 0.2 or greater than the discount interval 20, the condition is judged to be met, and the monitoring thread is activated.
Optionally, the obtaining of the discount information of the product may specifically include:
acquiring initial discount information of a commodity;
if the user account number has a plurality of coupons, the coupons are matched with the commodities one by one;
and if the matching is successful, calculating the final discount price of the successfully matched commodity, wherein the final discount price is the initial discount of the accumulated commodity and the discount of the discount coupon. For example, the current user has 5 coupons, which are coupon information of basketball, football, badminton, table tennis and billiards, wherein the coupon information of basketball is 2, the first coupon is full 100 minus 25 yuan, and the second coupon is 8.8 discount coupon. When the current recommended commodity is a basketball, the cloud calculator automatically matches a basketball coupon to be applied to the basketball commodity, the initial discount of the basketball commodity is 9 folds, after the coupon is overlapped, if the price is higher than 100 yuan, 25 yuan is subtracted from 9 folds, or 9 folds is 8.8 folds, and the lower value of the two is taken; if the price is below 100 dollars, the final price is 9 x 8.8 folds (without considering the coupon mutual exclusion problem).
S102, judging whether the discount degree of the commodity is higher than a preset threshold value or not;
s103, if the value is higher than a preset threshold value, judging whether the commodity is a to-be-recommended commodity;
wherein, whether the commodity is the recommended commodity or not is judged, which may be specifically:
recording the purchasing behavior of the user, and constructing a user portrait based on the purchasing record;
and based on the user portrait, adopting a recommendation algorithm based on an association rule to screen commodities, and screening the commodities to be recommended from a plurality of commodities.
The item to be recommended may be a combination of items purchased by the user, that is, an item combination, or may be a similar item to the item purchased by the user.
The commodity combination is a sales pattern formed based on big data mining technology. For example, baby diapers are usually combined with baby milk, a pencil and an eraser may form a combination, and if a user purchases the baby diapers, the baby milk may be recommended as a combined product. It can be seen that the combined commodity is usually different products under the same large requirement of the user, different types of products can form different groups of commodity combinations, and the commodity combinations which are high-frequency and fit with the actual requirements of the user can be continuously updated through a large data mining technology.
The recommendation algorithm based on the association rule is based on user purchasing behavior and user behavior analysis of big data, and the currently purchased commodities and the user portrait are analyzed, so that the commodity combination is recommended.
From the aspect of algorithm, the following core concepts need to be explained:
item set: if users with different characteristics are classified, different sets need to be divided to represent user groups with different characteristics, namely item sets, the item sets are sets of items, and different items represent different user groups. A set containing 0 or more items is an item set, and if k items are contained, it is referred to as a k item set.
Confidence (confidence): if the group buying the infant diapers simultaneously is an item set m, the group buying the infant milk powder is an item set n, and the group buying the infant diapers and the infant milk powder simultaneously is an item set X, then in order to measure the degree of correlation between the two behaviors of buying the infant diapers and buying the infant milk powder, the behavior of buying the infant diapers and the degree of correlation between buying the infant diapers and the infant milk powder simultaneously can be represented by dividing X by m, and similarly, the behavior of buying the infant milk powder and the degree of correlation between buying the infant diapers and the infant milk powder simultaneously can be represented by dividing X by n. A sentence summary: the confidence is the number of people who buy multiple products at the same time divided by the sales volume of a product, which reflects the correlation between the product and other products.
Support (supuport): now there is a degree of correlation between the commodities, then is this degree of correlation true? A steady measuring standard is needed, and if the number of people who buy the infant diapers and the infant milk powder at the same time is divided by the total number of people, the proportion of the number of people who buy the infant diapers and the infant milk powder at the same time in the crowd can be obtained, the proportion takes the total number of the crowd as the measuring standard, and whether the number of people X has a general meaning can be reflected, namely: the number of people who buy x and y at the same time is divided by the total number of people, and then the support degree can be obtained, and the reliability of the support degree reaction rule can be obtained.
Through a certain degree of correlation sorting, the degree of correlation among different commodities can be obtained, which is a mining method of a frequent item set based on rules, and algorithms commonly used for mining the frequent item set are Apriori and FP-growth. Compared with the Apriori algorithm, the FP-growth algorithm only needs to traverse the database twice, thereby efficiently finding frequent item sets.
Similar commodities are commodities with the same or similar types as those purchased by a user, and in a big data recommendation algorithm, a collaborative filtering algorithm is generally adopted for recommending the similar commodities.
Collaborative filtering is largely divided into two broad categories, one broad category being user-based and the other broad category being commodity-based. User-based collaborative filtering recommendation: the method mainly focuses on users, the users are divided into a plurality of categories according to the similarity degree, and what users in the categories buy is recommended to the current users. The other is a product-based filtering recommendation: when a user searches for something, the products searched by the user are diffused, and more related similar products are recommended to the user. At the same time, these similar products or other related products are ordered. And then obtaining the final recommendation result. In the collaborative filtering algorithm, the key point is the calculated relation basis-users or products can obtain different results; another aspect is a method of calculating similarity. The similarity calculation methods mainly include the following methods: consine similarity, Pearson correlation coefficient, Jaccard similarity.
S104, if the commodity is to-be-recommended, calculating a commodity recommendation index based on a big data line;
optionally, the index is x based on the commodity recommendation on the big data line, and satisfies
Figure BDA0002548493380000081
Figure BDA0002548493380000082
Wherein, a is the purchasing behavior index of the user, b is the weight proportion, c is the correction parameter, d is the price factor, and f (e) is the product combination logic function. Wherein f (e) has different settings according to the specific website, the specific time, and the type of the specific product.
And S105, generating commodity recommendation information according to the recommendation index, and sending the commodity recommendation information to the user side so that the user side can purchase commodities according to the discount information and the recommendation index.
The commodity recommendation information may include a priority of commodity recommendation, and if the recommendation index value is large, the priority is high, and the commodity recommendation can be preferentially performed.
According to the method for recommending the commodities on the big data line, the price is monitored by setting the monitoring program, the priority of discount commodity recommendation is determined based on the recommendation index, and the discount commodity recommendation is recommended to the user for purchase, so that the commodity recommendation efficiency is optimized, the commodity recommendation success rate is improved, and the user experience is improved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (1)

1. A commodity recommendation method based on a big data line is characterized by comprising the following steps:
the cloud server activates a monitoring thread, monitors prices of a plurality of commodities and acquires initial discount information of the commodities;
if the user account number has a plurality of coupons, matching the coupons with the commodities one by one;
if the matching is successful, calculating the final discount price of the successfully matched commodity, wherein the final discount price is the initial discount of the commodity and the discount of the coupon;
judging whether the discount degree of the commodity is higher than a preset threshold value or not;
if the user image is higher than the preset threshold value, recording the purchasing behavior of the user, and constructing the user image based on the purchasing record;
based on the user portrait, adopting a recommendation algorithm based on an association rule to screen commodities, and screening commodities to be recommended from a plurality of commodities; the to-be-recommended commodity is a combined commodity of commodities purchased by the user;
if the commodity is a to-be-recommended commodity, calculating the commodity recommendation index based on the big data line; the commodity recommendation index based on the big data line is x and meets the requirement of the index
Figure FDA0002886286060000011
A is a user purchasing behavior index, b is a weight proportion, c is a correction parameter, d is a price factor, and f (e) is a product combination logic function;
generating commodity recommendation information according to the recommendation index, and sending the commodity recommendation information to a user side so that the user side can purchase commodities according to the discount information and the recommendation index;
before activating the monitoring thread, the method further comprises:
estimating the price of the commodity in a specific period based on a historical change curve of the commodity price;
judging whether the discount degree of the estimated price is higher than the preset threshold value or not;
and if the threshold value is higher than the preset threshold value, activating a monitoring thread.
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CN113065919A (en) * 2021-04-08 2021-07-02 北京京东乾石科技有限公司 Data pushing method and device
CN113379516A (en) * 2021-08-12 2021-09-10 永正信息技术(南京)有限公司 Recommended product determination method and device

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