TW201734909A - Method and apparatus for identifying target user - Google Patents

Abstract

Embodiments of the present application provide a method and apparatus for identifying a target user. The method comprises: obtaining user information of a user to be identified, the user information comprising behavior information and association information; for the behavior information, determining a behavior index of the user to be identified; for the association information, determining an association index of the user to be identified; and determining whether the user to be identified is a target user according to the behavior index and the association index. Calculation modes of a behavior index and an association index are specified, and the identification method can be standardized and quantified, so that the identification of a target user is more accurate and effective.

Description

Target user identification method and device

The present invention relates to the field of internet technology, and more particularly to a method for identifying a target user and a device for identifying a target user.

The advancement of Internet technology has promoted the development of e-commerce. Through the Internet, users can quickly purchase the products they need. Therefore, the marketing of goods by merchants is increasingly moving from offline to online.

In e-commerce marketing, social marketing is an important marketing tool. The traditional social marketing is to establish contact with the buyer through the e-commerce platform or the seller, and then to recommend the new customer by the buyer. This kind of marketing method has many shortcomings. One is that it requires more manpower operation and it costs a lot. The second is that the effect is poor, because the platform or the seller is not clear whether the buyer is authoritative and whether it has sufficient influence, thus making marketing The input and output are relatively low.

Therefore, if you can find users with sufficient authority and influence under the platform or a certain category in a timely manner, you can target and quality services by personalizing and focusing on the operation of the user, thus affecting other consumers. The purpose is to make marketing more targeted and more effective.

In view of the above problems, embodiments of the present invention have been made in order to provide an object user identification method and a corresponding target user identification device that overcome the above problems or at least partially solve the above problems.

In order to solve the above problem, the present invention discloses a method for identifying a target user, including: acquiring user information of a user to be identified, the user information including behavior information and associated information; and determining, for the behavior information, the user to be identified a behavior index; and, for the association information, determining an association index of the user to be identified; determining, according to the behavior index and the association index, whether the user to be identified is a target user.

Optionally, the behavior information includes a user behavior number and a user behavior object, and the step of determining the behavior index of the to-be-identified user for the behavior information includes: determining the to-be-identified for the user behavior number The number index of the user; determining, for the user behavior object, an object index of the user to be identified; determining the behavior index by using the number of times index and the object index.

Optionally, the determining the to-be-identified for the number of times of the user behavior The step of indexing the number of times of the user includes: determining whether the number of times of user behavior in the preset time period is greater than a first preset threshold; if yes, determining that the number of times index is a first number of times index; if not, determining the The number index is the second number index.

Optionally, the user behavior object includes a first attribute behavior object and a second attribute behavior object, and the step of determining an object index of the to-be-identified user for the user behavior object includes: targeting the first attribute Determining, by the behavior object, a first attribute behavior object index of the user to be identified; determining, for the second attribute behavior object, a second attribute behavior object index of the user to be identified; using the first attribute behavior object index and The second attribute behavior object index determines an object index of the user to be identified.

Optionally, before the step of determining the first attribute behavior object index of the to-be-identified user for the first attribute behavior object, the method further includes: determining whether the first attribute behavior object is included in the user behavior object If so, the time of occurrence of the user behavior for the first attribute behavior object is determined.

Optionally, the determining, by the first attribute behavior object, the first attribute behavior object index of the to-be-identified user includes: acquiring the first attribute behavior object that occurs earlier than a preset time The number of the first attribute behavior objects that occur earlier than the preset time in the user behavior object according to the number of the first attribute behavior objects.

Optionally, the determining, by the second attribute behavior object, the second attribute behavior object index of the user to be identified includes: acquiring the quantity of the second attribute behavior object; according to the second attribute behavior The number of objects determining the proportion of the second attribute behavior object in the user behavior object.

Optionally, the step of determining the behavior index by using the number of times index and the object index comprises: weighting the number of times index, the first attribute behavior object index, and the second attribute behavior object index Summing and obtaining the behavior index.

Optionally, the association information includes the number of associated users and associated object information, and the determining, for the associated information, the association index of the to-be-identified user includes: determining, according to the number of the associated users, the a user association index of the user to be identified; determining, for the associated object information, an object association index of the user to be identified; and determining the association index by using the user association index and the object association index.

Optionally, the determining the waiting for the number of the associated users The step of identifying the user association index of the user includes: determining whether the number of the associated users is greater than a second preset threshold; if yes, determining that the user association index is a first user association index; if not, determining the user association The index is the second user association index.

Optionally, the determining, according to the associated object information, the object association index of the user to be identified includes: calculating, for the associated object information, the similarity between the user to be identified and other users; The similarity determines an object association index of the user to be identified.

Optionally, the determining, according to the similarity, the object association index of the user to be identified includes: finding a number of users whose similarity with the user to be identified is greater than a third preset threshold; The number of users determines an object association index of the user to be identified.

Optionally, the step of determining the association index by using the user association index and the object association index comprises: weighting the user association index and the object association index to obtain an association of the user to be identified index.

Optionally, the determining, according to the behavior index and the association index, whether the user to be identified is a target user includes: Sorting the behavior index and the correlation index separately, obtaining a behavior index ranking ratio and a correlation index ranking ratio; ranking the behavior index ranking ratio with a first preset ratio, and/or ranking the correlation index Comparing the two preset ratios; determining, according to the comparison result, whether the user to be identified is a target user.

In order to solve the above problem, the present invention discloses a target user identification device, including: a user information acquisition module, configured to acquire user information of a user to be identified, the user information including behavior information and associated information; and a behavior index determination module And determining, according to the behavior information, the behavior index of the user to be identified; and the association index determining module, configured to determine, according to the association information, a correlation index of the user to be identified; a target user identification module And determining, according to the behavior index and the association index, whether the user to be identified is a target user.

Optionally, the behavior information includes a user behavior number and a user behavior object, and the behavior index determination module includes: a number index determination sub-module, configured to determine the number of the user to be identified for the number of user behaviors. An index; an object index determining submodule, configured to determine an object index of the user to be identified for the user behavior object; and a behavior index determining submodule, configured to determine the behavior by using the number index and the object index index.

Optionally, the number-of-times index determining sub-module includes: a user behavior number determining sub-module, configured to determine whether the number of user behaviors in the preset time period is greater than a first preset threshold; The module is configured to determine, when the number of user behaviors in the preset time period is greater than the first preset threshold, that the number of times index is the first number of times index; and the second number of times index determining the sub-module for using the preset time When the number of user behaviors in the segment is less than the first preset threshold, it is determined that the number of times index is the second number of times index.

Optionally, the user behavior object includes a first attribute behavior object and a second attribute behavior object, and the object index determination submodule includes: a first attribute behavior object index determination submodule, configured to be used for the first The attribute behavior object, the first attribute behavior object index of the user to be identified is determined; the second attribute behavior object index determination submodule is configured to determine, for the second attribute behavior object, the second attribute of the user to be identified a behavior object index; a user object index determining submodule to be identified, configured to determine an object index of the user to be identified by using the first attribute behavior object index and the second attribute behavior object index.

Optionally, the object index determining submodule further includes: a first attribute behavior object determining submodule, configured to determine whether the first attribute behavior object is included in the user behavior object; and a user behavior occurrence time determining submodule Used for the user behavior When the first attribute behavior object is included in the object, the time of occurrence of the user behavior for the first attribute behavior object is determined.

Optionally, the first attribute behavior object index determining submodule includes: a first attribute behavior object quantity obtaining submodule, configured to acquire the number of the first attribute behavior objects whose occurrence time is earlier than a preset time; a first attribute behavior object ratio determining submodule, configured to determine, according to the quantity of the first attribute behavior object, that the first attribute behavior object whose occurrence time is earlier than the preset time is in the user behavior object The proportion.

Optionally, the second attribute behavior object index determining submodule includes: a second attribute behavior object quantity obtaining submodule, configured to acquire the quantity of the second attribute behavior object; and the second attribute behavior object proportion determining unit And a module, configured to determine, according to the quantity of the second attribute behavior object, a proportion of the second attribute behavior object in the user behavior object.

Optionally, the behavior index determining submodule includes: a behavior index weighted summation submodule, configured to weight the number of times index, the first attribute behavior object index, and the second attribute behavior object index Summing and obtaining the behavior index.

Optionally, the association information includes the number of associated users and associated object information, and the association index determining module includes: a user association index determining submodule, configured to be used by the associated user. a quantity determining a user association index of the user to be identified; an object association index determining submodule, configured to determine an object association index of the user to be identified for the associated object information; and a correlation index determining submodule for The association index is determined using the user association index and the object association index.

Optionally, the user association index determining submodule includes: an associated user quantity determining submodule, configured to determine whether the number of the associated users is greater than a second preset threshold; the first user association index determining submodule, And determining, when the number of the associated users is greater than a second preset threshold, that the user association index is a first user association index; and the second user association index determining submodule, where the number of the associated users is less than When the second preset threshold is used, the user association index is determined to be a second user association index.

Optionally, the object association index determining submodule includes: a similarity calculation submodule, configured to calculate a similarity between the user to be identified and other users one by one for the associated object information; the user object to be identified The association index determining submodule is configured to determine an object association index of the user to be identified according to the similarity.

Optionally, the to-be-identified user object association index determining sub-module includes: a user quantity searching unit, configured to find a number of users whose similarity with the to-be-identified user is greater than a third preset threshold; Unit for determining the number of users The object association index of the user to be identified is determined.

Optionally, the association index determining sub-module includes: an association index weighted summation sub-module, configured to weight the user association index and the object association index to obtain a correlation index of the user to be identified.

Optionally, the target user identification module includes: an index sorting sub-module, configured to respectively sort the behavior index and the correlation index, obtain a behavior index ranking ratio and an association index ranking ratio; and sort ratio comparison sub-module a group, configured to compare the behavior index ranking ratio with a first preset ratio, and/or, the correlation index ranking ratio is compared with a second preset ratio; the target user identification sub-module is configured to determine according to the comparison result Whether the user to be identified is a target user.

Compared with the prior art, the embodiment of the present invention includes the following advantages: the embodiment of the present invention determines the user index of the user to be identified, and then determines the behavior index and the association index of the user to be identified according to the user information, thereby determining Whether the user to be identified is a target user can standardize and quantify the identification method, so that the identification of the target user is more accurate and effective.

Secondly, the embodiment of the present invention determines the authority index by determining the behavior index of the user's purchase of the commodity (such as the favorable rate) and the time point of purchasing the commodity, and determines the authority index by the strong relationship and the weak relationship of the user, and determines the user. Whether it has influence, among which, weak relationship adopts commodity phase It seems to be related, which can specifically characterize the potential influence of users on others, further clarify the calculation method of user behavior index and association index, help to quickly identify the target users, and help the merchants to conduct social based on the identified target users. Marketing, operational selection and category recommendation, etc., make the marketing and recommendation of the business more targeted.

501‧‧‧User Information Acquisition Module

502‧‧‧ Behavior Index Determination Module

503‧‧‧Association Index Determination Module

504‧‧‧Target User Identification Module

1 is a flow chart of a first embodiment of a method for identifying a target user of the present invention; FIG. 2 is a flow chart of a second embodiment of a method for identifying a target user according to the present invention; FIG. 3 is a flowchart of determining a user of the present invention. Schematic diagram of the behavior index; FIG. 4 is a schematic diagram showing the growth of a certain commodity sales amount of the present invention; and FIG. 5 is a structural block diagram of an embodiment of the identification device of the target user of the present invention.

The present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.

Referring to FIG. 1 , a flow chart of a first embodiment of a method for identifying a target user according to the present invention is shown. The method may include the following steps: Step 101: Obtain user information of a user to be identified. In the embodiment of the present invention, User information can include behavioral information And associated information.

The behavior information may be a recorded behavior of the user, such as an introduction information of a user browsing an item, collecting the item in a favorite, or purchasing the item.

The association information may be other users directly associated with the user, for example, the user's friend information; or other users indirectly associated with the user, for example, information of other users who have browsed or purchased a certain product. .

Step 102: Determine, according to the behavior information, a behavior index of the user to be identified; Step 103, determine an association index of the user to be identified for the association information; in the embodiment of the present invention, obtain the user's After the behavior information and the associated information, the behavior index and the association index of the user to be identified may be determined separately for the behavior information and the associated information.

Step 104: Determine, according to the behavior index and the association index, whether the user to be identified is a target user.

In a specific implementation, the behavior index and the association index may be directly added, and the result is compared with a preset judgment condition to identify whether the user is a target user, and the behavior index and the association index may be respectively given different degrees. The weight value, and then the result of the weighted summation is judged to identify the target user, and the behavior index and the association index are respectively compared with the preset judgment conditions to obtain a judgment of whether the user to be identified is the target user, and the prior art People can choose how to choose according to actual needs. The target user is identified, and the present invention does not specifically limit this.

In the embodiment of the present invention, the user information of the user to be identified is obtained, and then the behavior index and the association index of the user to be identified are determined according to the user information, thereby determining whether the user to be identified is the target user, Standardization and quantification of the identification method are implemented, making the identification of the target user more accurate and effective.

Referring to FIG. 2, a flow chart of the steps of the second embodiment of the method for identifying the target user of the present invention is shown. The method may include the following steps: Step 201: Obtain user information of the user to be identified. In the embodiment of the present invention, User information can include behavioral information and associated information of users on an e-commerce platform.

The behavior information may be a recorded behavior of the user, such as an introduction information of a user browsing an item, collecting the item in a favorite, or purchasing the item.

The association information may be other users directly associated with the user, for example, the user's friend information; or other users indirectly associated with the user, for example, information of other users who have browsed or purchased a certain product. .

As shown in FIG. 3, it is a schematic diagram of determining a user's behavior index according to the present invention. In a specific implementation, user information can be obtained through a data warehouse of the e-commerce platform. The data warehouse is generally used for reading and writing data, and can store user transaction information sheets, commodity transaction information sheets, and product DSR (Detail Seller Rating) information sheets.

Step 202: Determine, according to the number of times of the user behavior, the to-be-identified The number of times of the user; in a specific implementation, the number of user behaviors may be user transaction volume data.

In a preferred embodiment of the present invention, the step of determining the number of times of the user to be identified for the number of times of the user behavior may specifically include the following sub-steps: sub-step 2021, determining that the preset time period is within the preset time period Whether the number of user behaviors is greater than a first predetermined threshold; sub-step 2022, if yes, determining that the number of times index is a first number of times index; sub-step 2023, if not, determining that the number of times index is a second number of times index.

In the embodiment of the present invention, determining the number of times of the user to be identified according to the number of times of the user behavior may be obtained by comparing the number of user behaviors in the preset time period with a preset threshold. Specifically, the preset time period may be 30 days, 90 days, or 180 days, etc., and the length of the preset time period may be determined by a person skilled in the art according to actual needs, which is not specifically limited by the present invention.

For example, the transaction amount data of the user to be identified within 90 days may be extracted, and then compared with a first preset threshold. If the transaction amount is greater than the first preset threshold, the number of times index may be determined as the first number of times. The index may be determined to be the second number index if the transaction amount is less than or equal to the first preset threshold. The first preset threshold may be specifically set according to the length of the preset time period, for example, if the preset time period is set to 30 days, The first preset threshold may be set to 10 times, that is, the transaction amount is 10 times within 30 days, and if the preset time period is set to 90 days, the first preset threshold may be set to 45 times accordingly. , that is, the transaction volume reached 45 times in 90 days.

Generally, the first number of times index should be greater than the second number of times index. For example, if the first number of times index is set to 0.8, the second number of times index can be set accordingly to be 0.5. The size of the first order index and the second number of times index may also be determined by a person skilled in the art according to actual needs, which is not specifically limited in the present invention.

Step 203: Determine, according to the user behavior object, an object index of the user to be identified. In the embodiment of the present invention, the user behavior object may include a first attribute behavior object and a second attribute behavior object. For example, when the user behavior object is a commodity, the first attribute behavior object may be a commodity with a high sales volume (explosive product), and the second property behavior object may be a commodity with a high favorable rate (excellent product).

In a preferred embodiment of the present invention, the step of determining the object index of the user to be identified for the user behavior object may specifically include the following sub-steps: sub-step 2031, for the first attribute behavior object Determining the first attribute behavior object index of the user to be identified; in the embodiment of the present invention, before determining the first attribute behavior object index according to the first attribute behavior object, the user may be first determined Whether the first attribute behavior object is included in the behavior object, that is, the user Whether the purchased product includes a product with a high sales amount, and if so, the occurrence time of the user behavior for the first attribute behavior object may be further determined. The user behavior can occur at a specific time when the user purchases such a high-volume item.

Therefore, the sub-step of determining the first attribute behavior object index of the user to be identified may further include: acquiring the first attribute behavior object whose occurrence time is earlier than a preset time, for the first attribute behavior object a quantity; determining, according to the number of the first attribute behavior objects, a proportion of the first attribute behavior object whose occurrence time is earlier than the preset time in the user behavior object.

In the embodiment of the present invention, the preset time may be a time when the sales volume growth rate reaches a certain value. As shown in FIG. 4, it is a schematic diagram of the increase in sales volume of a certain product of the present invention, wherein the horizontal axis is the time (unit: day) at which the product is sold on the line, and the vertical axis is the sales volume. As can be seen from the figure, the sales volume increased slowly on the first 1-3 days, and after the third day, due to various reasons (such as better product quality, pre-sponsor self-promotion, promotion, repeat customers), the sales volume of goods increased. It became noticeably faster, and the growth rate reached its maximum on the 6th-7th day. After the 7th day, for some reasons (such as the emergence of market replicas, competitors, etc.), the sales growth rate began to fall.

If the maximum speed increase is S _max , the time point at which the speed increase is αS _max is set as the critical point T, where α <1, when the user's purchase time t<=T, the The user has purchased the product before it becomes a blast. For example, for Figure 4, assume that the sales volume reaches the maximum growth rate on the 7th day, at this time S _max = 120%, taking α = 1/2, then αS _max = 60%, assuming that the growth rate is 60% at the time point On the fourth day, the preset time is the fourth day (the critical point T=4). For a different product category, the preset time may be different, and a specific value of the critical point may be determined by a person skilled in the art according to actual needs, which is not specifically limited in the present invention.

Then, the number of items whose purchase time is earlier than the preset time can be obtained, and then the proportion of the item in all the items is determined.

For example, if a user purchases a total of 10 items, 3 of them are high-selling items (explosives), and one of the 3 items is purchased before it becomes a blast ( That is, if the purchase time has a preset time/critical point, the proportion of the goods with a higher sales volume in all commodities is 1/10=0.1.

Sub-step 2032, determining, for the second attribute behavior object, a second attribute behavior object index of the user to be identified; in the embodiment of the present invention, the second attribute behavior object may be a commodity with a high favorable rate ( For example, a product having a favorable value exceeding a certain value r (r<=100%), a person skilled in the art can determine the specific size of r according to actual needs, which is not specifically limited in the present invention.

In a preferred embodiment of the present invention, the determining, by the second attribute behavior object, the sub-step of determining the second attribute behavior object index of the user to be identified may further include: acquiring the second attribute behavior object The number of the second genus is determined according to the number of the second attribute behavior object The proportion of sexual behavior objects in the user behavior object.

In the specific implementation, if r=99%, the product with a favorable rate of 99% or more can be regarded as a high-quality product, and the number of products with a favorable rate of 99% or more can be obtained from the user information, and then the product is determined to be The proportion of all goods.

For example, if a user purchases a total of 10 items, and the favorable rate of 9 items is more than 99%, it can be determined that the proportion of the products with higher favorable rate in all the products is 9/10=0.9.

Sub-step 2033, determining the object index of the user to be identified by using the first attribute behavior object index and the second attribute behavior object index.

In a specific implementation, the first attribute behavior object index and the second attribute behavior object index may be directly added to obtain an object index of the user to be identified, and the first attribute behavior object index may also be The second attribute behavior object index is respectively assigned different weight values, and then the result of the weighted summation is used as the object index of the user to be identified. A person skilled in the art can select the manner in which the object index of the user to be identified is determined according to actual needs, which is not specifically limited in the present invention.

Step 204: Determine the behavior index by using the number of times index and the object index. In a preferred embodiment of the present invention, the step of determining the behavior index by using the number of times index and the object index may specifically include The following sub-steps: sub-step 2041, the number of times index, the first attribute behavior The object index, and the second attribute behavior object are exponentially weighted and summed to obtain the behavior index.

In a specific implementation, the behavior index of the user to be identified may be calculated by using the following formula: score = w ₁ a + w ₂ b + w ₃ c

Where a is the index of the number of times the user is to be identified, b is the first attribute behavior object index, c is the second attribute behavior object index, and w ₁ , w ₂ , w ₃ are the weight values of a, b, and c, respectively. and _{w 1 + w 2 + w 3} = 100, those skilled in the art can, on a weight value of the weight is adjusted according to the needs of the business objectives, the present disclosure is not particularly limited.

For example, if the number of times of the user to be identified is a=0.5, the first attribute behavior object index b=0.1, the second attribute behavior object index c=0.9, and w ₁ =30, w ₂ =50, w ₃ =20 The behavior index of the user to be identified is score=30*0.5+50*0.1+20*0.9=38.

Step 205: Determine, according to the number of the associated users, the user association index of the user to be identified. In the embodiment of the present invention, the number of the associated users may be the number of friends of the user on the e-commerce platform, and the relationship between the friends It reflects the strong relationship between users.

In a preferred embodiment of the present invention, the step of determining the user association index of the to-be-identified user for the number of the associated users may specifically include the following sub-steps: sub-step 2051, determining the associated user's Whether the quantity is greater than a second preset threshold; Sub-step 2052, if yes, determining that the user association index is a first user association index; sub-step 2053, and if not, determining that the user association index is a second user association index.

In a specific implementation, the number of friends of the user to be identified may be compared with a preset threshold to determine a user association index. Specifically, the preset threshold may be 100, 150, or 180, etc., and a person skilled in the art may determine the size of the preset threshold according to actual needs, which is not specifically limited by the present invention.

For example, the second preset threshold may be set to 150, and then the number of friends of the user to be identified is compared with a second preset threshold. If the number of friends is greater than the second preset threshold, the user association index may be determined to be the first. A user association index may be determined to be a second user association index if the number of friends is less than or equal to a second predetermined threshold.

Generally, the first number of times index should be greater than the second number of times index. For example, user A has 100 buyer friends, and user B has 200 buyer friends. When the second preset threshold is 150, the number can be A user association index is set to 0.7, and the first user association index is set to 0.4 correspondingly, that is, user A's user association index is 0.4, user B's user association index is 0.8, and the first user association index and the second user association index are The size may also be determined by a person skilled in the art according to actual needs, and the present invention does not specifically limit this.

Step 206: Determine an object association index of the user to be identified for the associated object information. In the embodiment of the present invention, the associated object information may be information determined according to a user behavior object, for example, a certain item is purchased at the same time.

In a preferred embodiment of the present invention, the step of determining the object association index of the user to be identified for the associated object information may specifically include the following sub-steps: sub-step 2061, for the associated object information, Calculating the similarity between the user to be identified and other users one by one; in the embodiment of the present invention, the similarity between users can be calculated according to the product purchased by the user, and when the similarity reaches a certain level, the two users can be considered There is a weak relationship between them.

In a preferred embodiment of the present invention, the similarity between the user to be identified and other users may be calculated one by one by using the following formula:

Where |M| is the number of user behavior objects of the user to be identified, |N| is the number of user behavior objects of other users; |MN| is the number of identical user behavior objects owned by the user to be identified and other users.

For example, if user 1 purchases {a, b, c} three items, user 2 purchases {a, c, d} three items, then user 1 and user 2 jointly purchase two items: a and c , according to the above formula, there are:

In the embodiment of the present invention, each user of the e-commerce platform may be scanned to calculate the similarity between the user to be identified and each of the other users.

Sub-step 2062, determining an object association index of the user to be identified according to the similarity.

In a preferred embodiment of the present invention, the sub-step of determining the object association index of the user to be identified according to the similarity may further include: finding that the similarity with the user to be identified is greater than the third pre- The number of users of the threshold is determined; and the object association index of the user to be identified is determined according to the number of users.

In a specific implementation, when the similarity reaches the third preset threshold β (for example, β =0.8), a weak relationship is established between the users, and then the number of users whose similarity with the user to be identified is greater than 0.8 is found. Then, according to the number of users, an object association index of the user to be identified is determined. A specific size of the third predetermined threshold value β can be determined by a person skilled in the art according to the needs of the service, which is not specifically limited in the present invention.

Step 207: Determine the association index by using the user association index and the object association index. In a preferred embodiment of the present invention, the user association index and the object association index are used to determine the association index. The step may specifically include the following sub-steps: sub-step 2071, associating the user association index with the object The index is weighted and summed to obtain the association index of the user to be identified.

In a specific implementation, the correlation index of the user to be identified may be calculated by using the following formula: score ₂ = w ₁ P + w ₂ Q

Where P is the user association index of the user to be identified, Q is the object association index, w ₁ , w ₂ are the weight values of P and Q, respectively, and w ₁ + w ₂ = 100, which can be based on the service by those skilled in the art. The weight value is adjusted according to the needs of the specific target, and the present invention does not specifically limit this.

For example, if the user association index of the user to be identified is P=0.4, Q=0.8, and w ₁ =60, w ₂ =40, the association index of the user to be identified can be obtained as score ₂ = 60*0.4+40* 0.8=56.

Step 208: Determine, according to the behavior index and the association index, whether the user to be identified is a target user.

After the behavior index and the association index of the user to be identified are respectively obtained, whether the user to be identified is the target user may be determined according to the behavior index and the association index.

For example, the behavior index of the user to be identified may be added to the association index, and the user to be identified may be identified according to the obtained result, and the user may also be identified according to the behavior index or the association index, respectively.

In a preferred embodiment of the present invention, the step of determining whether the user to be identified is a target user according to the behavior index and the association index may specifically include the following sub-steps: sub-step 2081, The behavior index and the correlation index are respectively sorted, and the behavior index ranking ratio and the correlation index ranking ratio are obtained; Sub-step 2082, comparing the behavior index ranking ratio with a first preset ratio, and/or comparing the association index ranking ratio with a second preset ratio; sub-step 2083, determining the to-be-identified according to the comparison result Whether the user is the target user.

In the embodiment of the present invention, the obtained behavior index and the association index of all users may be separately sorted, and the behavior index ranking ratio and the association index ranking ratio are obtained. For example, the behavior index and association may be in descending order. The index is sorted. If the total number of users is M, the behavior index of the user to be identified is ranked Nth among all users, and the association index ranks Kth among all users, then the ranking ratio of the behavior index is N/M*100. %, the correlation index sorting ratio is K/M*100%, and then the behavior index ranking ratio is compared with the first preset ratio, and/or the correlation index sorting ratio is compared with the second preset ratio. According to the comparison result, it is determined whether the user to be identified is a target user.

In a specific implementation, the behavior index ranking ratio may be first compared with the first preset ratio. If it is determined that the behavior index ranking ratio is within the second preset ratio range, then the correlation index ranking ratio and the second preset are further determined. a ratio, determining whether the relevance index ranking ratio is within a second preset ratio range, and if yes, determining that the to-be-identified user is a target user, or first determining that the correlation index ranking ratio is within a second preset ratio range Then, the behavior index ranking ratio is compared with the first preset ratio, and according to the comparison result, it is determined whether the user to be identified is the target user. Those skilled in the art can determine the order of comparison according to actual needs, or simultaneously The ranking ratio of the behavior index and the first preset ratio, and the ranking ratio of the association index are compared with the second preset ratio, which is not specifically limited by the present invention.

In addition, in some specific cases, the embodiment of the present invention may also identify the target user by using only the ranking index of the behavior index or the association index, which is not specifically limited by the present invention.

In the embodiment of the present invention, the first preset ratio and the second preset ratio may be the same or different, for example, the first preset ratio and the second preset ratio may be set to 10%, or A preset ratio is set to 10%, and a second preset ratio is set to 8%. A specific value of the first preset ratio and the second preset ratio may be determined by a person skilled in the art according to actual needs, which is not specifically limited by the present invention.

In the embodiment of the present invention, the behavior index is determined by determining the behavior index of the user's purchase of the commodity (such as the favorable rate) and the time point of purchasing the commodity, and determining whether the user is authoritative; determining the association index by the strong relationship and the weak relationship of the user, and determining whether the user has the Influence, in which the weak relationship is similar to the commodity, it can specifically represent the potential influence of the user on others, further clarify the calculation method of the user behavior index and the association index, which helps to quickly identify the target user and helps The merchants conduct social marketing, operational selection and category recommendation based on the identified target users, which makes the marketing and recommendation of the merchant more targeted.

It should be noted that, for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should understand that the embodiments of the present invention are not limited by the described action sequence, because According to an embodiment of the invention, certain steps may be in other orders or At the same time. In the following, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

Referring to FIG. 5, a block diagram of a structure of an apparatus for identifying a target user of the present invention is shown. The method may include the following module: a user information obtaining module 501, configured to acquire user information of a user to be identified, the user The information includes the behavior information and the associated information; the behavior index determining module 502 is configured to determine the behavior index of the user to be identified for the behavior information; and the association index determining module 503 is configured to: Determining the association index of the user to be identified; the target user identification module 504, configured to determine, according to the behavior index and the association index, whether the user to be identified is a target user.

In the embodiment of the present invention, the behavior information may include a user behavior number and a user behavior object, and the behavior index determination module 502 may specifically include the following sub-module: a frequency index determination sub-module 5021, configured to The number of times of the user, the number of times of the user to be identified is determined; the object index determining sub-module 5022 is configured to determine an object index of the user to be identified for the user behavior object; the behavior index determining sub-module 5023, The behavior index is determined by using the number of times index and the object index.

In the embodiment of the present invention, the number-of-times index determining sub-module 5021 may specifically include the following sub-modules: The user behavior number determining sub-module 211 is configured to determine whether the number of user behaviors in the preset time period is greater than a first preset threshold; the first-time index determining sub-module 212 is configured to be within the preset time period. When the number of user behaviors is greater than the first preset threshold, determining that the number of times index is the first number of times index; the second number of times index determining sub-module 213 is configured to use the number of user behaviors within the preset time period to be less than the first preset At the threshold, the number of times index is determined to be a second number of indices.

In the embodiment of the present invention, the user behavior object may include a first attribute behavior object and a second attribute behavior object, and the object index determination sub-module 5022 may specifically include the following sub-module: the first attribute behavior object index is determined. a sub-module 221, configured to determine, according to the first attribute behavior object, a first attribute behavior object index of the user to be identified, and a second attribute behavior object index determination sub-module 222, configured to target the second attribute a behavior object, determining a second attribute behavior object index of the user to be identified; a user object index determination sub-module 223 to be identified, configured to determine the first attribute behavior object index and the second attribute behavior object index The object index of the user to be identified.

In the embodiment of the present invention, the object index determining sub-module 5022 may further include the following sub-module: the first attribute behavior object determining sub-module 224, configured to determine whether the first attribute behavior is included in the user behavior object. Object The user behavior occurrence time determining sub-module 225 is configured to determine a time of occurrence of the user behavior for the first attribute behavior object when the first attribute behavior object is included in the user behavior object.

In the embodiment of the present invention, the first attribute behavior object index determining sub-module 221 may specifically include the following sub-module: the first attribute behavior object quantity obtaining sub-module 221A, configured to acquire the occurrence time earlier than the pre- The first attribute behavior object ratio determining sub-module 221B is configured to determine, according to the quantity of the first attribute behavior object, that the occurrence time is earlier than the preset time The proportion of an attribute behavior object in the user behavior object.

In the embodiment of the present invention, the second attribute behavior object index determining sub-module 222 may specifically include the following sub-module: a second attribute behavior object quantity obtaining sub-module 222A, configured to acquire the second attribute behavior object. The second attribute behavior object ratio determining sub-module 222B is configured to determine, according to the quantity of the second attribute behavior object, a proportion of the second attribute behavior object in the user behavior object.

In the embodiment of the present invention, the behavior index determining sub-module 5023 may specifically include the following sub-module: a behavior index weighted summation sub-module 231, configured to use the number-of-times index, the first attribute behavior object index, And, the second attribute behavior object is exponentially weighted and summed to obtain the behavior index.

In the embodiment of the present invention, the association information may include an associated user. The number and the associated object information, the association index determining module 503 may specifically include the following sub-module: a user association index determining sub-module 5031, configured to determine the user of the user to be identified for the number of the associated users a correlation index; an object association index determining sub-module 5032, configured to determine an object association index of the user to be identified for the associated object information; a correlation index determining sub-module 5033, configured to adopt the user association index and the object A correlation index that determines the association index.

In the embodiment of the present invention, the user association index determining sub-module 5031 may specifically include the following sub-module: the associated user number determining sub-module 311, configured to determine whether the number of the associated users is greater than a second preset threshold. a first user association index determining submodule 312, configured to determine, when the number of the associated users is greater than a second preset threshold, that the user association index is a first user association index; and the second user association index determines a submodule The group 313 is configured to determine that the user association index is a second user association index when the number of the associated users is less than a second preset threshold.

In the embodiment of the present invention, the object association index determining sub-module 5032 may specifically include the following sub-module: the similarity calculation sub-module 321 is configured to calculate the user to be identified one by one for the associated object information. The similarity between other users; the user object association index determining sub-module 322 to be used for determining the object association index of the user to be identified according to the similarity.

In the embodiment of the present invention, the similarity between the user to be identified and other users may be calculated one by one by using the following formula:

Where |M| is the number of user behavior objects of the user to be identified, |N| is the number of user behavior objects of other users; |MN| is the number of identical user behavior objects owned by the user to be identified and other users.

In the embodiment of the present invention, the to-be-identified user object association index determining sub-module 322 may specifically include the following unit: a user quantity searching unit 322A, configured to find that the similarity with the to-be-identified user is greater than a third preset. The number of users of the threshold; the object association index determining unit 322B is configured to determine an object association index of the user to be identified according to the number of users.

In the embodiment of the present invention, the association index determining sub-module 5033 may specifically include the following sub-module: an association index weighted summation sub-module 331, configured to weight and sum the user association index and the object association index. Obtaining an association index of the user to be identified.

In the embodiment of the present invention, the target user identification module 504 may specifically include the following sub-module: an index ordering sub-module 5041, configured to respectively sort the behavior index and the correlation index to obtain a behavior index ranking ratio. And the index of the association index; a sorting ratio comparison sub-module 5042, configured to compare the behavior index ranking ratio with a first preset ratio, and/or the correlation index sorting ratio is compared with a second preset ratio; the target user identification sub-module 5043, And determining, according to the comparison result, whether the user to be identified is a target user.

For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.

The various embodiments in the present specification are described in a progressive manner, and each embodiment focuses on differences from other embodiments, and the same similar parts between the various embodiments can be referred to each other.

Those skilled in the art will appreciate that embodiments of the embodiments of the invention may be provided as a method, apparatus, or computer program product. Thus, embodiments of the invention may take the form of a complete hardware embodiment, a full software embodiment, or an embodiment combining soft and hardware aspects. Moreover, embodiments of the present invention may employ computer program products embodied on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) having computer usable code therein. form.

In a typical configuration, the computer device includes one or more processors (CPUs), input/output interfaces, a network interface, and memory. The memory may include non-permanent memory, random access memory (RAM) and/or non-volatile memory in a computer readable medium such as read only memory (ROM) or flash memory ( Flash RAM). Memory is an example of a computer readable medium. Computer readable media includes both permanent and non-permanent Sexual, actionable and inactive media can be stored by any method or technique. The information can be computer readable instructions, data structures, modules of programs, or other materials. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), and other types of random access memory (RAM). Read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM only, digital versatile disc (DVD) or other optical storage, magnetic tape cartridge, tape storage or other magnetic storage device or any other non-transportable media that can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include non-persistent computer readable media, such as modulated data signals and carrier waves.

Embodiments of the invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. The computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine such that instructions are executed by a processor of a computer or other programmable data processing terminal device Means are provided for implementing the functions specified in one or more of the flow or in one or more blocks of the flow chart.

These computer program instructions can also be stored in a bootable computer or other The programmable data processing terminal device is readable in a computer readable memory in a specific manner, such that the instructions stored in the computer readable memory generate an article of manufacture including the instruction device, the instruction device being implemented in one or more of the flowcharts The functions specified in a block or blocks of a flow and/or block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device such that a series of operational steps are performed on a computer or other programmable terminal device to produce computer implemented processing for use in a computer or other programmable terminal device The instructions executed above provide steps for implementing the functions specified in one or more blocks of the flowchart or in a block or blocks of the flowchart.

While a preferred embodiment of the present invention has been described, it will be apparent that those skilled in the art can make various changes and modifications to the embodiments. Therefore, the scope of the appended claims is intended to be construed as a

Finally, it should also be noted that in this context, relational terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities. There is any such actual relationship or order between operations. Furthermore, the terms "comprises" or "comprising" or "comprising" or any other variations are intended to encompass a non-exclusive inclusion, such that a process, method, article, or terminal device that includes a plurality of elements includes not only those elements but also Other elements that are included, or are included for such a process, method, item, or end The elements inherent in the end device. An element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that comprises the element, without further limitation.

The method for identifying a target user and the identification device for a target user provided by the present invention are described in detail. The principle and implementation manner of the present invention are described in the following. The description of the above embodiment is only used. To help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in specific embodiments and application scopes. The content should not be construed as limiting the invention.

Claims (28)

A method for identifying a target user includes: obtaining user information of a user to be identified, the user information including behavior information and associated information; determining, for the behavior information, a behavior index of the user to be identified; and, for the association And determining, by the information, the association index of the user to be identified; determining, according to the behavior index and the association index, whether the user to be identified is a target user. The method of claim 1, wherein the behavior information includes a user behavior number and a user behavior object, and the step of determining the behavior index of the to-be-identified user for the behavior information comprises: targeting the user behavior The number of times, the index of the number of times the user to be identified is determined; for the user behavior object, the object index of the user to be identified is determined; and the behavior index is determined by using the number of times index and the object index. The method of claim 2, wherein the determining the number of times of the user to be identified for the number of times of the user behavior comprises: determining whether the number of user behaviors in the preset time period is greater than the first preset a threshold; if yes, determining that the number of times index is the first number of times index; If not, it is determined that the number of times index is the second number of times index. The method of claim 2 or 3, wherein the user behavior object comprises a first attribute behavior object and a second attribute behavior object, and for the user behavior object, determining an object index of the user to be identified The step of: determining, for the first attribute behavior object, a first attribute behavior object index of the user to be identified; and determining, for the second attribute behavior object, a second attribute behavior object index of the user to be identified; The first attribute behavior object index and the second attribute behavior object index determine an object index of the user to be identified. The method of claim 4, wherein before the step of determining the first attribute behavior object index of the user to be identified for the first attribute behavior object, the method further comprises: determining the user behavior object Whether the first attribute behavior object is included; if so, the time of occurrence of the user behavior for the first attribute behavior object is determined. The method of claim 5, wherein the determining, by the first attribute behavior object, the first attribute behavior object index of the user to be identified comprises: acquiring the occurrence time earlier than a preset time The number of first attribute behavior objects; determining the occurrence time according to the number of the first attribute behavior objects The proportion of the first attribute behavior object that is earlier than the preset time in the user behavior object. The method of claim 5 or 6, wherein the determining, for the second attribute behavior object, the second attribute behavior object index of the user to be identified comprises: acquiring the second attribute behavior object A quantity; determining, according to the number of the second attribute behavior objects, a proportion of the second attribute behavior object in the user behavior object. The method of claim 7, wherein the determining the behavior index using the number of times index and the object index comprises: the number of times index, the first attribute behavior object index, and the The second attribute behavior object is exponentially weighted and summed to obtain the behavior index. The method of claim 1 or 2 or 3 or 5 or 6 or 8, wherein the associated information includes the number of associated users and associated object information, and for the associated information, determining the user to be identified The step of associating the index includes: determining, for the number of the associated users, a user association index of the user to be identified; determining, for the associated object information, an object association index of the user to be identified; using the user association index and An object association index that determines the association index. The method of claim 9, wherein the Determining, by the number of the associated users, the user association index of the to-be-identified user: determining whether the number of the associated users is greater than a second preset threshold; if yes, determining that the user association index is a first user association index; If not, it is determined that the user association index is a second user association index. The method of claim 10, wherein the determining, for the associated object information, the object association index of the user to be identified comprises: calculating, for the associated object information, the user to be identified and others The similarity between the users; determining the object association index of the user to be identified according to the similarity. The method of claim 11, wherein the determining the object association index of the user to be identified according to the similarity comprises: finding that the similarity with the user to be identified is greater than a third preset threshold The number of users; determining the object association index of the user to be identified according to the number of users. The method of claim 12, wherein the step of determining the association index using the user association index and the object association index comprises: And weighting the user association index and the object association index to obtain an association index of the user to be identified. The method of claim 10 or 11 or 12 or 13, wherein the determining, according to the behavior index and the association index, whether the user to be identified is a target user comprises: The correlation indexes are respectively sorted, and the behavior index ranking ratio and the association index ranking ratio are obtained; the behavior index ranking ratio is compared with the first preset ratio, and/or the correlation index ranking ratio is compared with the second preset ratio. And determining, according to the comparison result, whether the user to be identified is a target user. A target user identification device, comprising: a user information acquisition module, configured to acquire user information of a user to be identified, the user information includes behavior information and associated information; and a behavior index determination module, configured to use the behavior information, Determining a behavior index of the user to be identified; and a correlation index determining module, configured to determine, according to the association information, a correlation index of the user to be identified; a target user identification module, configured to use the behavior index and The association index determines whether the user to be identified is a target user. The device of claim 15, wherein the behavior information includes a user behavior number and a user behavior object, and the behavior index determination module includes: a number index determination sub-module, configured to target the user behavior times, Determining a number of times of the user to be identified; an object index determining submodule, configured to determine an object index of the user to be identified for the user behavior object; and a behavior index determining submodule for using the number index And an object index to determine the behavior index. The apparatus of claim 16, wherein the number of times index determining sub-module comprises: a user behavior number determining sub-module, configured to determine whether the number of user behaviors in the preset time period is greater than a first preset threshold; The first number index determining submodule is configured to determine that the number of times index is the first number of times index when the number of user behaviors in the preset time period is greater than the first preset threshold; the second number index determining submodule And determining, when the number of user behaviors in the preset time period is less than a first preset threshold, determining the number of times index as the second number of times index. The apparatus of claim 16 or 17, wherein the user behavior object comprises a first attribute behavior object and a second attribute behavior object, the object index determination submodule comprising: a first attribute behavior object index determining submodule a group, configured to determine, for the first attribute behavior object, a first attribute behavior object index of the to-be-identified user; and a second attribute behavior object index determination sub-module, configured to determine, for the second attribute behavior object, a second attribute behavior object index of the user to be identified; The user object index determining sub-module to be used is configured to determine an object index of the user to be identified by using the first attribute behavior object index and the second attribute behavior object index. The device of claim 18, wherein the object index determining sub-module further comprises: a first attribute behavior object determining sub-module, configured to determine whether the first attribute behavior object is included in the user behavior object; The behavior occurrence time determining sub-module is configured to determine a time of occurrence of the user behavior for the first attribute behavior object when the first attribute behavior object is included in the user behavior object. The device of claim 19, wherein the first attribute behavior object index determining submodule comprises: a first attribute behavior object quantity obtaining submodule, configured to acquire the first occurrence time that is earlier than a preset time a quantity of the attribute behavior object; the first attribute behavior object ratio determination submodule, configured to determine, according to the quantity of the first attribute behavior object, that the first attribute behavior object whose occurrence time is earlier than the preset time is The proportion of the user behavior object. The apparatus of claim 19 or 20, wherein the second attribute behavior object index determining submodule comprises: a second attribute behavior object quantity obtaining submodule, configured to acquire the number of the second attribute behavior object a second attribute behavior object ratio determining submodule, configured to determine, according to the quantity of the second attribute behavior object, the second attribute behavior object The proportion of user behavior objects. The device of claim 21, wherein the behavior index determining sub-module comprises: a behavior index weighted summation sub-module, configured to use the number of times index, the first attribute behavior object index, and The second attribute behavior object is exponentially weighted and summed to obtain the behavior index. The device of claim 15 or 16 or 17 or 19 or 20 or 22, wherein the associated information includes the number of associated users and associated object information, and the associated index determining module includes: a user association index determining submodule a group, configured to determine, according to the number of the associated users, a user association index of the user to be identified; an object association index determining submodule, configured to determine an object association index of the user to be identified for the associated object information And a correlation index determining submodule for determining the association index by using the user association index and the object association index. The device of claim 23, wherein the user association index determining sub-module comprises: an associated user number determining sub-module, configured to determine whether the number of the associated users is greater than a second preset threshold; the first user The association index determining sub-module is configured to determine that the user association index is a first user association index when the number of the associated users is greater than a second preset threshold; and the second user association index determining sub-module is configured to When the number of the associated users is less than a second preset threshold, determining that the user association index is the first Two user association index. The apparatus of claim 24, wherein the object association index determining sub-module comprises: a similarity calculation sub-module, configured to calculate, for the associated object information, one by one between the user to be identified and another user The similarity degree is determined by the user object association index determining submodule, and is used to determine an object association index of the user to be identified according to the similarity. The device of claim 25, wherein the to-be-identified user object association index determining sub-module comprises: a user quantity searching unit, configured to find that the similarity with the to-be-identified user is greater than a third preset threshold The number of users; an object association index determining unit, configured to determine an object association index of the user to be identified according to the number of users. The apparatus of claim 26, wherein the association index determination sub-module comprises: an association index weighted summation sub-module, configured to weight-sum the user association index and the object association index, to obtain the The association index of the user to be identified. The device of claim 24 or 25 or 26 or 27, wherein the target user identification module comprises: an index ordering sub-module for respectively sorting the behavior index and the correlation index to obtain a behavior index Sorting ratio and correlation index sorting ratio; sorting ratio comparison sub-module for sorting the behavior index And the first preset ratio, and/or the correlation index sorting ratio is compared with the second preset ratio; the target user identification sub-module is configured to determine, according to the comparison result, whether the to-be-identified user is the target user.