TWI515676B - Recommended methods and systems for product information - Google Patents

Description

Product information recommendation method and system

The invention relates to a data processing technology, in particular to a method and a system for recommending product information.

In Internet technology, websites often need to recommend various product information to users. For example, an e-commerce website recommends products that may be of interest to users on a web page. With this recommended method, the user can shorten the path of finding the desired product and improve the user experience.

Generally, when the website performs the recommendation of the product, the correlation algorithm is used to determine the relationship between the other products and the purchased product according to the user's historical operation metadata for some products, such as the user's product purchase history data. Therefore, the product information with strong relevance to the product purchased by the user is recommended to the user.

However, this recommendation method only considers the user's historical operation metadata, and does not comprehensively consider other information associated with the product that the user is interested in. Therefore, the recommendation result is often very inaccurate; in particular, when the user is a new user, Since there is no historical operation metadata, it is even difficult to recommend the product to the user at this time.

Moreover, the existing correlation algorithm itself consumes a large amount of system resources, and all the products need to be calculated with other products, and the amount of processed data is large and slow, especially in In the case of massive users, massive products, and massive access data, the processing speed of data is slow and the resource consumption is more serious, which makes it difficult to meet the timeliness requirements of the recommendation system.

In view of this, the technical problem to be solved by the present invention is to provide a method and system for recommending product information, which can recommend product information that may be needed to the user in a timely and accurate manner.

To this end, the embodiment of the present invention adopts the following technical solution: The embodiment of the present invention provides a method for recommending product information, including: predetermining a recommended product set of a user and/or a recommended product set of the product; and acquiring a network operation of the first user. Determining a product recommendation type according to the network operation of the first user; determining, according to the determined product recommendation type, the corresponding product set from the first user and/or the recommended product concentration of the first product associated with the network operation The product information recommended by the first user is required under the product recommendation type.

A recommendation system for providing product information, comprising: a first determining unit, configured to predetermine a recommended product set of the user and/or a recommended product set of the product; and a second determining unit, configured to acquire the first user The network operation determines the product recommendation type according to the network operation of the first user, and the third determining unit is configured to select the recommended product set of the first user and/or the network operation according to the determined product recommendation type. The recommended products of a product focus on the product information recommended for the first user under the corresponding product recommendation type.

The technical effects of the above technical solutions are analyzed as follows: a recommended product set of the user and the product is determined in advance, and the product recommendation for the user is divided into at least two types of recommendation, thereby determining a product recommended for the user according to the network operation of the user. The recommendation type, and then the product information recommended by the user is determined according to the product recommendation type, thereby improving the accuracy of recommending the product information for the user; and, according to the user's various characteristic information, the product characteristic information, and the user within a certain period of time According to the information of the products concerned, the recommended product set of each user and the recommended product set of each product are determined accordingly, and since the user and the product characteristic information are comprehensively considered in the recommended method, the recommendation result is compared with the prior art. It is more reasonable and accurate. Moreover, by establishing the recommended product set, even if the new user performs network operations, or the user performs network operations on the new product, the product can be based on the user or the product based on the auxiliary recommended product set. Recommend to implement related products for new users or new products When the product recommendation is made, the basic recommended product set of the user and the product is determined based on the data within a preset time period, and the maximum recommended product quantity of the basic recommended product set is limited; A product that meets a certain threshold number of basic product sets, or a product that determines the number of browsing thresholds within a certain period of time to determine a basic recommended product set, thereby greatly reducing the amount of data of the basic recommended product set, and reducing the system The requirements of resources have improved the speed of product recommendation. Even in the case of massive users, massive products, and massive product data, it is possible to recommend products to users in a timely manner.

The product information recommendation method of the present invention does not necessarily have all of the above effects.

Hereinafter, the method and system for recommending the product information of the present invention will be described in detail with reference to the accompanying drawings.

In the network structure shown in FIG. 1, the user communicates with the server 12 by the client 11 to obtain product information of the product of interest from the server 12; and the server 12 can also be located to the user. The client 11 returns the product information recommended to the user.

As shown in FIG. 1, in an actual application, there may be multiple users accessing the server 12 by different clients. Correspondingly, the server 12 needs to return the product information recommended to the corresponding user to the user end where each user is located.

As shown in FIG. 2, the server 12 performs the following steps:

Step 201: Predetermine a recommended product set of each user and/or a recommended product set of each product; the recommended product set is composed of several products. The number of products in the recommended product set can be set autonomously, and is not limited herein.

The recommended product set may include: a basic recommended product set and/or an auxiliary recommended product set, and a basic recommended product set and an auxiliary recommended product set construction method will be described in detail in the embodiment of FIG. 3, and details are not described herein.

Step 202: Obtain a network operation of the first user, and determine a product recommendation type according to a network operation of the first user; the product recommendation type may include: a product recommendation based on the user and a product recommendation based on the product.

The user-based product recommendation refers to recommending a product that may be of interest to the user based on the user's preference information and historical access behavior.

The product-based product recommendation refers to: recommending related products for products currently of interest to the user based on the correlation between the products.

Step 203: Determine, according to the determined product recommendation type, the recommended product set of the first user and/or the recommended product set of the first product associated with the network operation, and determine the first user recommendation under the corresponding product recommendation type. Product information.

Wherein, when the product recommendation type is a user-based product recommendation, the product information that needs to be recommended for the user is determined from the user's recommended product set; when the product recommendation type is the product-based product recommendation, the recommended product set of the product is determined from the product. Product information recommended for the user.

In the recommendation method shown in FIG. 2, the recommended product set of the user and the product is determined in advance, and the product recommendation for the user is divided into at least two recommendation types, thereby determining the recommended product recommendation for the user according to the user's network operation. The type, in turn, determines the product information that is recommended for the user based on the type of product recommendation, thereby improving the accuracy of recommending product information to the user.

Hereinafter, the product recommendation method of the present invention will be described in more detail with reference to FIG. 3 on the basis of FIG.

As shown in FIG. 3, the method includes:

Step 301: Determine characteristic information of each user, characteristic information of each product, information about the degree of attention of each user in the preset first time period, and each user in a preset second time period. Product attention information.

Each user's characteristic information can include: user's source area, preferred product subcategory, price range, brand, style, color, material, user activity, user integrity and other attribute fields.

The characteristic information of each product may include: sub-category, price, brand, style, color, material, information quality rating, hot sales degree, attention degree, release time and other attribute fields of the product.

The user's attention to the product includes: the value of each user's attention to various products and the source area of the user.

The length of the first time period may be set autonomously, for example, may be one month or 10 days, 20 days, etc., and is not limited herein. Here, it is possible to determine the characteristic information of each user and the characteristic information of each product by statistical analysis and data mining based on information such as user information and behavior.

In practical applications, the characteristic information of all users and the characteristic information of all products can be stored in the form of a database, for example, a user characteristic database is established to store characteristic information of each user; and product characteristic data is established. Library to store information about the characteristics of each product.

Step 302: Determine, according to the above information, a recommended product set of each user and a recommended product set of each product.

Specifically, each user's recommended product set may include: a basic recommended product set and/or an auxiliary recommended product set.

The method for determining the basic recommended product set of each user may include:

Obtaining a sub-category of the preferred product corresponding to the user from the characteristic information of the user; searching for all products of the sub-category of the preferred sub-category according to the characteristic information of the product; selecting a second preset number from the found products The products constitute the basic recommended product set for the user.

Alternatively, the determining method of the basic recommended product set of each user may include: obtaining a sub-category of the preferred product corresponding to the user from the characteristic information of the user; and searching for the sub-category belonging to the sub-category of the preferred product according to the characteristic information of the product. Product; and, according to the product attention information of each user in the preset first time period, the correlation between the user and other users is calculated; according to the product attention information of each user in the preset second time period Finding a product that is most relevant to the user and a third number of users in the second time period; selecting a second predetermined number of products from all the found product information to form a basic recommendation product of the user set.

Among them, when determining the correlation between users, it can be implemented using a user-based collaborative filtering algorithm.

In a specific implementation, in addition to the first time period being preset, in order to reduce the amount of data to be processed in the process of determining the basic recommendation product set of the user, the step of determining the basic recommended product set of the user may be further performed. Limiting, thereby reducing the amount of data of the user's basic recommended product set. Specifically, it can be determined whether the determined number of products in the user-based recommended product set exceeds a certain preset number threshold, and if not exceeded, the user's basis is not determined. The recommended product set, that is, for the user whose basic recommended product quantity does not exceed a certain threshold, the basic recommended product set of the user is not established; only the user whose basic recommended product quantity exceeds the threshold of the number establishes the basic recommendation of the user. Product set. For users who do not have a basic recommended product set, the user's product recommendation needs to be based on the user's auxiliary recommended product set.

The determining the auxiliary recommended product set of each user includes: obtaining a source area of the user from the characteristic information of the user; and searching for the selling degree and/or attention of the product belonging to the source area of the user according to the characteristic information of the product; The fourth predetermined number of products with the highest degree of degree and/or release time constitutes the user's auxiliary recommended product set.

For each product, the recommended product set may also include: a basic recommended product set, or a basic recommended product set and an auxiliary recommended product set. The pre-determining the basic recommendation result set of each product may include: calculating, according to each user's attention information of the product in the preset first time period, the correlation between the products; for each product, selecting The first predetermined number of products that are most relevant to the product constitute the basic recommended product set for the product.

Among them, the product association rule recommendation algorithm and the product relevance recommendation algorithm can be implemented when determining the correlation between products.

The same as the user's basic recommended product set determination process, when determining the basic recommended product set of the product, the product that needs to establish the basic recommended product set may also be screened. Specifically, the product may be judged within a preset time period. Whether the number of views exceeds a preset threshold of browsing. When not exceeding, the basic recommended product set is not determined for the product; when exceeded, the basic recommended product set of the product is determined. For a product that does not have a basic recommended product set, it is necessary to determine the recommended product of the product by the auxiliary recommended product set of the product.

The determining the auxiliary recommended product set of each user includes: determining characteristic information of each user and characteristic information of each product; for each user, obtaining a source area of the user from the characteristic information of the user; The feature information finds that the fourth predetermined number of products belonging to the source region of the user, the popularity and/or the degree of attention and/or the release time constitutes the user's auxiliary recommended product set.

The determining the auxiliary recommended product set of the product comprises: determining, according to the product attention information of each user in the preset first time period, the fifth preset number of product components under the sub-category with the highest degree of interest in each source region is based on Auxiliary recommendation result set for the product.

The above steps 301 and 302 are preparation steps for the server to respond to the user's network operation. Hereinafter, the process of recommending the product according to the user's network operation:

Step 303: Acquire a network operation of the first user.

The first user refers to any user who performs network operations.

The network operation may include: the user opens a webpage provided by the server for the user, the user clicks to view a certain product in the webpage, purchases a certain product, and the like.

Step 304: Determine, according to the network operation of the first user, a product recommendation type that is required to be provided for the first user.

Wherein, when the user's network operation does not involve the product, the determined product recommendation type is generally: based on the user's product recommendation, for example, the user opens a certain webpage provided by the server for the user.

When the user's network operation involves the product, such as when the user clicks to view a certain product in the webpage or purchases a certain product, the determined product recommendation type may be: based on the user's product recommendation and/or product-based product. recommend.

When the product recommendation type is a user-based product recommendation, it is described by steps 305 to 306; when the product recommendation type is a product-based product recommendation, it is described by steps 307 to 308. Of course, in the actual application, step 305 to step 306 and/or step 307 to step 308 are determined according to the product recommendation type determined in step 304. Moreover, when it is determined in step 304 that both types of recommendation are performed, steps 305 to 306 and steps 307 to 308 may be performed simultaneously or sequentially, and the order of execution is not limited.

Step 305: Acquire a sixth preset number of products from the basic recommended product set of the first user; and, when the number of products in the basic recommended product set is less than the sixth preset number, obtain the auxiliary recommended product set from the first user. A difference in products is obtained to obtain the sixth predetermined number of products.

Wherein, when the auxiliary recommended product set is not preset, the step of obtaining the difference product will not be included.

Step 306: Sort the sixth preset number of products according to a preset first rule, and select a seventh preset number of products with the top position as the product information recommended by the first user.

Specifically, the ranking rule may be preset according to the preference characteristics of the user, such as the product priority of the price, brand, style, color, and material that meets the user's preference, and the priority of the product that the user has paid attention to in a certain period of time may be prioritized. Reduced so that the top-ranked products in the sorting results will be closer to the product of interest to the user.

Step 307: Acquire an eighth predetermined number of products from the basic recommended product set of the first product; and, when the number of products in the basic recommended product set is less than the eighth preset number, the auxiliary recommended product concentrates from the first product of the same type Obtaining a difference of products to obtain the eighth preset number of products; Step 308: Sorting the eighth preset number of products according to a preset second rule, and selecting a ninth preset number of the top positions The product serves as the product information recommended for the first user.

Specifically, when sorting, the sorting may be performed according to the correlation between the products, and the priority order of the products that the user has paid attention to in a certain period of time may be lowered, so that the products with the top positions in the sorting result are obtained. Will be closer to the product of interest to the user.

Step 309: Present the product information recommended by the first user to the user.

Among them, since the types of product recommendation are divided into two types, it is better to distinguish between the two types of recommendation when performing recommended product information display, so that the user can more clearly see the recommended product information.

For example, in an e-commerce webpage, a recommendation can be made when a user enters a list of purchased products, including a display bar of two product recommendations, and a "displayed by the user who purchased the product" display bar displays the product recommendation type based on the product. Product information, based on the products that are last added to the list of purchased products, and other related products, in order to achieve cross-selling between products; the "Other recommendations that may be of interest" display bar displays product information based on the user's product recommendation type. According to the characteristics of users, other products that may be of interest to users are recommended to further enhance the user's desire to purchase.

In addition, in practical applications, it is also possible to track and evaluate the recommendation effect of the product, for example, the number of exposures of the recommended product, the number of clicks, etc. can be obtained by logging the webpage; or, by accessing the recommended product database Transaction record, get the feedback quantity and volume of the recommended products. According to the following statistical indicators, the accuracy of the recommendation in each transaction link can be evaluated, and the effectiveness of the recommended application can be evaluated, and the recommendation algorithm can be optimized, which is not described here.

In the method shown in FIG. 3, according to various characteristic information of the user, characteristic information of the product, and information of the product of interest of the user in a certain period of time, the recommended product set of each user and the recommended product set of each product are determined accordingly. Therefore, when the user performs the network operation, the product information recommended by the user may be determined directly from the recommended product group corresponding to the user and/or the product according to the product operated by the user and/or the user, because comprehensive consideration is given in the recommended method. The user and product characteristics information, therefore, the recommendation results are more accurate than the prior art. Moreover, by establishing the set of recommended recommended products, even if the new user performs network operations, or the user operates the new product, the user can be recommended by the auxiliary recommended product set based on the user or based on the product, thereby implementing the new user or New products are recommended for related products. Compared with the existing recommendation system, only the historical operation is recommended, and the recommendation result of the present invention is more reasonable and accurate.

In addition, when performing product recommendation, the present invention determines the basic recommended product set of the user and the product based only on the data within a preset time period, and defines the maximum recommended product quantity of the basic recommended product set; The number of basic product sets meets a certain number of thresholds, or the product that has reached the threshold of a certain number of browsing times within a certain period of time determines the basic recommended product set, thereby greatly reducing the amount of data of the basic recommended product set, and reducing the resources for the basic recommended product set. The requirements have increased the speed of product recommendation. In the case of massive users, mass products, and massive product data, it is also possible to recommend products to users in a timely manner.

According to statistics, users and products with basic recommended product sets usually account for about 30% of all users and products. Furthermore, with stricter constraints, such as users who only meet a certain threshold number of basic product sets, Or, in a period of time, the number of times the number of browsing times reaches a certain number of browsing thresholds determines the basic recommended product set, and the amount of data of the basic recommended product set of the user and the product is greatly reduced. The auxiliary recommended product set is determined according to the user source area and the subcategory of the product. Since the number of user source areas and product subcategories is generally very limited, the performance of the recommended system is mainly determined by the amount of data of the basic recommended product set. . By the above processing of the present invention, the amount of data of the basic recommended product set is reduced to less than 1/3 of the total user and the product quantity, thereby greatly improving the product recommendation speed of the recommendation system (up to 3-5 times or even more) It also solves the problem of timeliness of product recommendation in the case of massive users, massive goods, and massive access data. Moreover, by applying statistical analysis, it is found that more than 85% of the users and products in each recommendation are based on the basic recommended product set, and only 15% of the new users and new products are recommended from the auxiliary recommended product set. Therefore, it is a good solution to the product recommendation problem of new and old users.

Corresponding to the above method, the present invention further provides a product information recommendation system. As shown in FIG. 4, the system includes: a first determining unit 41, configured to predetermine each user's recommended product set and/or each The recommended product set of the product; the second determining unit 42 is configured to obtain the network operation of the first user, determine the product recommendation type according to the network operation of the first user, and the third determining unit 43 is configured to determine the type of the product according to the determined product Determining product information recommended for the first user under the corresponding product recommendation type from the recommended product set of the first user and/or the recommended product set of the first product associated with the network operation.

The recommended product set may include: a basic recommended product set; or the recommended product set includes: a basic recommended product set and an auxiliary recommended product set.

Specifically, the first determining unit 41 may include: a first determining subunit, configured to determine a recommended product set of each user; and/or a second determining subunit, configured to determine a recommended product set of each product.

The first determining subunit may include: a first determining module, configured to determine characteristic information of each user and characteristic information of each product; and a first constituent module, configured for each user from the user Obtaining a product subcategory corresponding to the user in the characteristic information; searching for all products of the subcategory belonging to the subcategory of the preferred product according to the characteristic information of the product; selecting a second preset number of products from the found products to constitute the The user's basic recommended product set.

Alternatively, the first determining subunit may include: a second determining module, configured to determine characteristic information of each user, characteristic information of each product, product attention information of the user in a preset first time period, and a user a product attention information in a preset second time period; a third determining module, configured to obtain, for each user, a sub-category of the preferred product corresponding to the user from the characteristic information of the user; Finding all products whose subcategories belong to the sub-category of the preferred product; and calculating the correlation between the user and other users according to the product attention information of each user in the preset first time period; The product attention information in the second time period is set to find the product of the third time that the user with the highest relevance is the most concerned in the second time period; the second component module is used to find the Selecting a second predetermined number of products from all product information constitutes a basic recommended product set for the user.

The second determining sub-unit may include: a fourth determining module, configured to determine information about the degree of attention of the product for each user in the preset first time period; and a first calculating module, configured to use the information according to the attention level Calculating the correlation between the products; the third component module is configured to select, for each product, a first predetermined number of products having the highest correlation with the product to form a basic recommended product set of the product.

Preferably, the first determining subunit further includes: a fifth determining module, configured to determine characteristic information of each user and characteristic information of each product; and a fourth constituent module, configured for each user The user's characteristic information obtains the source region of the user; according to the product characteristic information, finds the fourth pre-product among the products belonging to the user's source region, the popularity and/or attention and/or the release time. A set of products constitutes a set of auxiliary recommended products for the user.

Preferably, the second determining sub-unit may further include: a fifth component module, configured to determine, according to the product attention information of each user in the preset first time period, the sub-category with the highest degree of attention in each source region. The fifth predetermined number of products constitutes a product-based auxiliary recommendation result set.

The product recommendation type includes: a user-based product recommendation and a product-based product recommendation. In this case, when the product recommendation type is a user-based product recommendation, the third determining unit 43 may include: a first acquisition sub- a unit, configured to obtain a sixth preset number of products from a first recommended product set of the first user; and, when the number of products in the basic recommended product set is less than the sixth preset number, from the first recommended auxiliary product set of the first user Obtaining a difference of products to obtain the sixth preset number of products; a first selecting subunit, configured to sort the sixth preset number of products according to a preset first rule, and select a top position of the sorting position Seven preset number of products are used as the product information recommended by the first user.

Alternatively, when the product recommendation type is a product-based product recommendation, the third determining unit 43 may include: a second obtaining sub-unit, configured to obtain an eighth preset number of products from the basic recommended product set of the first product; And, when the number of products in the basic recommended product set is less than the eighth preset number, the difference products are obtained from the first product auxiliary recommended products in the same category to obtain the eighth preset number of products; the second selector And a unit, configured to sort the eighth preset number of products according to a preset second rule, and select a ninth preset number of products with a top position as the product information recommended by the first user.

Preferably, the system may further include: a presentation unit 44, configured to present the product information recommended by the first user to the user.

For the above product recommendation system, the first determining unit predetermines the recommended product set of the user and the product, and divides the product recommendation for the user into at least two recommended types, so that the second determining unit determines that the user is determined according to the network operation of the user. The user performs the recommended product recommendation type, and the third determining unit determines the product information recommended by the user according to the product recommendation type, thereby improving the accuracy of recommending the product information for the user; and, according to the user's various characteristic information, the product The characteristic information and the information of the products that the user pays attention to within a certain period of time, according to which the recommended product set of each user and the recommended product set of each product are determined, since the characteristics information of the user and the product are comprehensively considered in the recommendation system, Therefore, the recommendation result is more reasonable and accurate than the prior art; and, by the establishment of the auxiliary recommended product set, even if the new user performs network operation, or the user performs network operation on the new product, the auxiliary recommendation can be recommended. Product set based on user or product-based product recommendation, implemented as new Household related products or new products recommendation.

In the above embodiment of the present invention, a plurality of preset materials, such as a first preset number, a second preset number, an eighth preset number, and the like, are not necessarily related to each other in actual application. The values of the respective materials may be the same or different, and are not limited herein.

A person skilled in the art can understand that the process of implementing the method of the foregoing embodiment can be completed by using a program instruction related hardware, and the program can be stored in a readable storage medium, and the program executes the above method when executed. The corresponding steps in . The storage medium may be, for example, a ROM/RAM, a magnetic disk, a compact disk, or the like.

The above is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make several improvements and retouchings without departing from the principles of the present invention. It should also be considered as the scope of protection of the present invention.

11. . . user terminal

12. . . server

41. . . First determining unit

42. . . Second determining unit

43. . . Third determining unit

44. . . Presentation unit

1 is an example of a network structure in an application scenario of the present invention;

2 is a schematic flow chart of a method for recommending product information according to the present invention;

3 is a schematic flow chart of another method for recommending product information according to the present invention;

FIG. 4 is a schematic structural diagram of a recommendation system of product information according to the present invention.

Claims (14)

A method for recommending product information, comprising: predetermining a recommended product set of a user and/or a recommended product set of the product; acquiring a network operation of the first user, determining a product recommendation type according to a network operation of the first user Determining, according to the determined product recommendation type, the product information recommended by the first user under the corresponding product recommendation type from the recommended product set of the first user and/or the recommended product set of the first product associated with the network operation . The method of claim 1, wherein the recommended product set comprises: a basic recommended product set and/or an auxiliary recommended product set. According to the method of claim 2, wherein the predetermined basic product set of the user includes: determining characteristic information of the user and characteristic information of the product; and for each user, obtaining the corresponding information of the user from the characteristic information of the user The product subcategory is preferred; according to the product characteristic information, all products belonging to the subcategory of the preferred product category are searched; and the second predetermined number of products selected from the found products constitute the basic recommended product set of the user. According to the method of claim 2, the predetermined basic product set of the user includes: determining user characteristic information, product characteristic information, product attention information of the user in a preset first time period, and Product attention information of the user in a preset second time period; for each user: obtaining a sub-category of the preferred product corresponding to the user from the characteristic information of the user; and finding the sub-category according to the characteristic information of the product belongs to the preference All products of the product sub-category; and, according to the product attention information of each user in the preset first time period, the correlation between the user and other users is calculated; according to each user in the preset second time period Product attention information, find the product with the highest relevance of the third highest number of users in the second time period; select the second preset number of products from all the found product information to constitute the product The user's basic recommended product set. According to the method of claim 2, wherein the predetermined recommendation result set of the predetermined product comprises: determining information about the user's attention to the product within a preset first time period; and calculating between the products according to the attention information Relevance; for each product, the first predetermined number of products that have the highest relevance to the product is selected to form the base recommended product set for the product. According to the method of claim 2, wherein the determining the user's auxiliary recommended product set comprises: determining the user's characteristic information and the product characteristic information; for each user, obtaining the user's source from the user's characteristic information. Region; according to the product characteristic information, the fourth predetermined number of products in the product belonging to the source region of the user, the popularity and/or attention and/or the release time constitute the auxiliary recommendation product of the user set. According to the method of claim 2, wherein the auxiliary recommended product set of the determined product comprises: determining a sub-class with the highest degree of interest in each source region according to product attention information of each user in a preset first time period. The fifth predetermined number of products currently constitutes a product-based auxiliary recommendation result set. The method of any one of claims 2 to 7, wherein the product recommendation type comprises: a user based product recommendation and a product based product recommendation. According to the method of claim 8, wherein when the product recommendation type is a user-based product recommendation, the product information recommended from the first user's recommended product set to be recommended by the first user includes: from the first The user's basic recommendation product collects a sixth preset number of products in a centralized manner; and when the number of products in the basic recommended product set is less than the sixth preset number, the difference product is obtained from the first user's auxiliary recommended product set to obtain the a sixth predetermined number of products; sorting the sixth preset number of products according to a preset first rule, and selecting a seventh predetermined number of products with a top position as the product information recommended by the first user . According to the method of claim 8, wherein when the product recommendation type is a product-based product recommendation, the recommended product set of the product related to the network operation determines the product information required to be recommended by the user, including: The basic recommended products of the first product collectively obtain the eighth predetermined number of products; and, when the number of products in the basic recommended product set is less than the eighth preset number, the difference products are obtained from the first product auxiliary recommended product products of the same type Acquiring the eighth preset number of products; sorting the eighth preset number of products according to the preset second rule, and selecting the ninth preset number of products in the top of the sorting position as the first recommended by the first user Product information. The method according to any one of the preceding claims, wherein the predetermined basic product set of the user further comprises: determining whether the determined number of products in the basic recommended product of the user exceeds a preset number threshold, When not exceeding, the basic recommended product set is not determined for the user. The method according to any one of claims 2 to 7, wherein the predetermined recommended product set of the predetermined product further comprises: determining whether the number of views of the product in a predetermined period of time exceeds a preset number of views When the threshold is not exceeded, the basic recommended product set is not determined for the product. The method of any one of claims 1 to 7, further comprising: presenting the product information recommended for the first user to the user. A product information recommendation system, comprising: a first determining unit, configured to predetermine a recommended product set of the user and/or a recommended product set of the product; and a second determining unit, configured to acquire the network of the first user The operation determines the product recommendation type according to the network operation of the first user, and the third determining unit is configured to: from the first product recommendation product set of the first user and/or the first product associated with the network operation according to the determined product recommendation type The recommended product set determines the product information recommended for the first user under the corresponding product recommendation type.