CN110992153A

CN110992153A - Commodity recommendation method, system and equipment based on user attributes and commodity types

Info

Publication number: CN110992153A
Application number: CN201911304899.5A
Authority: CN
Inventors: 慕畅
Original assignee: Shenzhen Montnets Encyclopedia Information Technology Co ltd
Current assignee: Shenzhen Montnets Encyclopedia Information Technology Co ltd
Priority date: 2019-12-17
Filing date: 2019-12-17
Publication date: 2020-04-10
Anticipated expiration: 2039-12-17
Also published as: CN110992153B

Abstract

The invention provides a commodity recommendation method based on user attributes and commodity types, which comprises the steps of firstly forming a user attribute-commodity type binary relation network by using attribute information of a user and type information of commodities purchased by the user, and then calculating the association weight, frequency weight, value weight, triggering centrality and network core degree of the commodities according to certain type of attribute information of the user; and then carrying out weighted summation on the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree to obtain the optimal node degree of the classified user commodities, and recommending the commodities to corresponding classified users according to the optimal node degree of the classified user commodities. The method can obtain the commodity which has the widest spread and the largest influence under a certain user attribute, and the commodity pushing is more detailed and accurate.

Description

Commodity recommendation method, system and equipment based on user attributes and commodity types

Technical Field

The invention relates to the field of data mining, in particular to a commodity recommendation method, a commodity recommendation system and commodity recommendation equipment based on user attributes and commodity types.

Background

The commodity recommendation method in the prior art usually adopts a collaborative filtering recommendation technology or a point degree centrality technology of a relationship network.

The drawbacks of collaborative filtering recommendations are: (1) it depends on the recommendation; that is, the user can only buy a certain amount of goods (in practical application, the buying can also represent the favorite selection conditions such as clicking, liking, selecting, collecting, adding shopping cart, etc.), and then the user can have the recommended target, which belongs to passive recommendation. (2) The recommended goods only consider single purchase (namely the accurate association behavior based on only one time), but not consider secondary derivative purchase, and the subsequent derivative recommendation effect is poor.

The defects of the dot-degree centrality of the relationship network are as follows: (1) only the transmissibility of the commodity (whether the commodity is linked with many other commodities) is considered, but the influence of the commodity is not considered. Firstly, although the commodity is associated with a plurality of commodities, the proportion of the number of times of associated and simultaneous purchase of the commodity to the total number of times of purchase of the commodity is low, for example, 10 times of total purchase of the commodity A are performed, 9 times of purchase are single purchase, and only 1 time of simultaneous purchase of other commodities is performed; secondly, although the commodity is connected with a plurality of other commodities, the purchasing frequency of the commodity is low, the commodity is not popular, the influence is poor, the possibility that the commodity is not purchased by audiences at all is high, and the overall effect is poor. Thirdly, although the commodity is related to a plurality of commodities, the total value of the commodity is low, and no money is made for a platform operator; (2) the refining degree is insufficient: the general recommendation does not distinguish the gender, income and other user attribute characteristics of the user, and the effect of the general recommendation possibly affects the users with different attributes to different degrees, so that the recommendation effect generates larger difference.

Disclosure of Invention

The embodiment of the invention aims to provide a commodity recommendation method, a system and equipment based on user attributes and commodity types, and aims to solve the problems that in the prior art, commodity factors are not considered comprehensively, and attribute characteristics of a user are not considered, so that the recommendation effect is poor and inaccurate.

A first object of an embodiment of the present invention is to provide a method for recommending a product based on a user attribute and a product type, where the method includes:

establishing a classified user commodity association information table according to the user commodity transaction data;

counting the total purchase frequency of each user category to each commodity; counting the associated purchase frequency of each user category to each commodity;

calculating the total value of each commodity according to the total purchase frequency of each commodity and the unit price of each commodity of each user category;

calculating the association weight, frequency weight and value weight of each commodity of each user category;

counting the frequency of the commodities of each user category which are purchased simultaneously according to the commodity transaction data of the users; creating a directional commodity purchasing frequency matrix of each user category;

creating an undirected commodity purchasing frequency matrix of each user category according to the directed commodity purchasing frequency matrix of each user category;

carrying out normalization processing on the directional commodity purchasing frequency matrixes of all user categories to obtain corresponding directional commodity purchasing weighting frequency matrixes;

calculating the triggering centrality of each commodity of each user category;

calculating the network core degree of each commodity of each user category;

respectively carrying out normalization processing on the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree of each commodity of each user category to obtain the corresponding weighted association weight, weighted frequency weight, weighted value weight, weighted triggering centrality and weighted network core degree;

weighting and summing the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree to obtain the optimal node degree for classifying the user commodities;

and recommending the commodities to the corresponding classified users according to the optimal node degree of the commodities of the classified users.

A second object of an embodiment of the present invention is to provide a product recommendation system based on user attributes and product types, the system including:

the user commodity associated information table creating module is used for creating a classified user commodity associated information table according to the user commodity transaction data;

the commodity purchase frequency counting module is used for counting the total purchase frequency of each commodity for each user category; counting the associated purchase frequency of each user category to each commodity;

the commodity total value calculating module is used for calculating the total value of each commodity according to the total purchase frequency of each user type to each commodity and the unit price of each commodity;

the weight calculation module is used for calculating the association weight, the frequency weight and the value weight of each commodity of each user category;

the oriented commodity purchase frequency matrix creating module is used for counting the oriented and simultaneous purchase frequency of commodities of various user categories according to the commodity transaction data of the users; creating a directional commodity purchasing frequency matrix of each user category;

the undirected commodity purchasing frequency matrix creating module is used for creating an undirected commodity purchasing frequency matrix of each user category according to the directed commodity purchasing frequency matrix of each user category;

the first normalization processing module is used for performing normalization processing on the directional commodity purchasing frequency matrixes of all user categories to obtain corresponding directional commodity purchasing weighting frequency matrixes;

the triggering centrality calculating module is used for calculating the triggering centrality of each commodity of each user category;

a network core degree calculation device for calculating the network core degree of each commodity of each user category;

the second normalization processing module is used for respectively normalizing the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree of each commodity of each user category to obtain the corresponding weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree;

the optimal node degree calculation module is used for carrying out weighted summation on the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree to obtain the optimal node degree of the classified user commodities;

and the commodity recommending module is used for recommending commodities to the corresponding classified users according to the optimal node degree of the commodities of the classified users.

It is a third object of an embodiment of the present invention to provide an apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the product recommendation method based on the user attribute and the product type when executing the computer program.

The invention has the advantages of

Drawings

Fig. 1 is a flowchart of a commodity recommendation method based on user attributes and commodity types according to an embodiment of the present invention;

fig. 2 is a flowchart of a method for calculating a network core degree of each commodity of each user category according to an embodiment of the present invention;

FIG. 3 is a commodity node G provided by the embodiment of the present invention₁、G₂、G₃、G₄、G₅A connection relationship network diagram of (1);

fig. 4 is a structural diagram of a product recommendation system based on user attributes and product types according to an embodiment of the present invention;

fig. 5 is a structural diagram of a network core degree computing device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and examples, and for convenience of description, only parts related to the examples of the present invention are shown. It is to be understood that the specific embodiments described herein are for purposes of illustration only and not for purposes of limitation, as other equivalent embodiments may be devised in accordance with the embodiments of the present invention by those of ordinary skill in the art without the use of inventive faculty.

Fig. 1 is a flowchart of a commodity recommendation method based on user attributes and commodity types according to an embodiment of the present invention; the method comprises the following steps:

step1, creating a classification user commodity association information table according to the user commodity transaction data;

the user commodity transaction data includes fields: user number, user category, commodity category and transaction time;

the classification user commodity association information table comprises the following fields: a user number, a user category and a plurality of specific commodity categories; when the value in the user commodity association information table indicates that one-time purchasing behavior occurs, whether the corresponding row user purchases the corresponding commodity or not is judged, 1 indicates purchasing, and 0 indicates not purchasing; each row in the table represents a purchase record; the same user can have multiple purchase records;

in a specific embodiment, the user number may be a number, a character, a combination thereof, or the like, which can uniquely mark the user identity, such as a mobile phone number, a card number, a serial number, or the like; the user category is classified according to user attributes or characteristics, such as gender, age, family income, region, and the like; the commodity transaction data is acquired from a business super or e-commerce platform and the like;

table 1 shows the user commercial transaction data segments according to the embodiment of the present invention; the user number is a card number, and the user category is gender; the commodity is a paid video, including a love paid video, a comprehensive art paid video and the like.

Card number	Sex	Commodity	Transaction time
				26630	Woman	Love	11-01 19:00
26630	Woman	Comprehensive art	11-01 19:20
				26630	Woman	Youth of youth	11-01 19:40
62995	Woman	Comprehensive art	11-02 17:50
				62995	Woman	Cartoon	11-02 19:00
38765	For male	Movement of	11-03 18:00
				…	...	...	...

TABLE 1

In particular embodiments, the purchased item may also indicate a favorite selection, viewed, selected, collected, shopping cart added, and the like; the lack of purchase may also mean dislike, lack of viewing, or lack of selection, etc., which one skilled in the art would understand is not intended to limit the scope of the present invention;

as shown in table 2, the present invention classifies the user commodity associated information table segments;

TABLE 2

Step2, counting the total purchase frequency of each commodity of each user category; counting the associated purchase frequency of each user category to each commodity;

in the embodiment of the invention, the commodity platform is set to have n commodities in total, and the set G ═ G is used₁，G₂，….G_nRepresents; n represents the number of commodity types, also called the number of commodity nodes; there are a total of k user categories, with the set C ═ C₁，c₂，….c_kDenotes, i ═ 1,2, 3.. n }, and x ═ 1,2, 3.. k };

user class c_xFor commodity G_iIs given by k × n matrix CF3 { (R3 (c)_x))_iDenotes (R3 (c)_x))_iThe value of (D) represents the user class c_xFor commodity G_iTotal frequency of purchases;

user class c_xFor commodity G_iIs given by k × n matrix CF2 { (R2 (c)_x))_iDenotes (R2 (c)_x))_iThe value of (D) represents the user class c_xFor commodity G_iAssociated purchase frequency of;

(R2(c_x))_i＝(R3(c_x))_i-(R1(c_x))_i(ii) a Wherein (R1 (c)_x))_iRepresenting a user class c_xFor commodity G_iIndividual purchase frequency of; commodity G_iThe individual purchase frequency of (A) indicates that only a single commodity G is purchased_iThe frequency of (2); commodity G_iThe associated purchase frequency of (2) indicates that the purchased product includes product G_iFrequency of (i.e. article G)_iFrequency of purchases at the same time with all other merchandise;

as shown in table 3, a data table of total purchase frequency of commodities for male and female users according to the embodiment of the present invention;

	love	Suspense questions of the present invention	Art and literature	Comprehensive art	Swordsman	Movement of	History of	Youth of youth	Crime	War	Cartoon
												For male	0	2	0	0	4	6	2	3	6	4	6
Woman	6	1	4	18	2	2	1	10	2	0	2

TABLE 3

As shown in table 4, a data table of commodity association purchase frequency for male and female users according to the embodiment of the present invention;

	love	Suspense questions of the present invention	Art and literature	Comprehensive art	Swordsman	Movement of	History of	Youth of youth	Crime	War	Cartoon
												For male	0	1	0	0	2	2	1	2	2	2	3
Woman	5	0	4	10	1	1	0	6	1	0	1

TABLE 4

Step3, calculating the total value of each commodity according to the total purchase frequency and the unit price of each commodity of each user type;

user class c_xPurchased article G_iThe total value of (a) is determined by k × n matrix CF4 { (Val3 (c)_x))_iDenotes (Val3 (c)_x))_iThe value of (D) represents the user class c_xPurchased article G_iThe total value of (c);

(Val3(c_x))_i＝(R3(c_x))_i×(Pr)_i；

as shown in table 5, a data table of total value of each commodity for the category of the male user according to the embodiment of the present invention;

	love	Suspense questions of the present invention	Art and literature	Comprehensive art	Swordsman	Movement of	History of	Youth of youth	Crime	War	Cartoon
												For male	0	2	0	0	4	6	2	3	6	4	6
Price per unit of goods	3	5	3	2	5	5	3	3	5	6	4
												Total value of goods	0	10	0	0	20	30	6	9	30	24	24

TABLE 5

As shown in table 7, a total value data table of each commodity for the category of the female user according to the embodiment of the present invention;

	love	Suspense questions of the present invention	Art and literature	Comprehensive art	Swordsman	Movement of	History of	Youth of youth	Crime	War	Cartoon
												Woman	6	1	4	18	2	2	1	10	2	0	2
Price per unit of goods	3	5	3	2	5	5	3	3	5	6	4
												Total value of goods	18	5	12	36	10	10	3	30	10	6	8

TABLE 6

Step4, calculating the association weight, frequency weight and value weight of each commodity of each user category;

wherein ,(W₁(c_x))_iRepresenting a user class c_xPurchased article G_iI.e. user class c_xFor commodity G_iAssociated purchase frequency (R2 (c)_x))_iIn the user class c_xFor commodity G_iTotal frequency of purchases (R3 (c)_x))_iThe ratio of (A) to (B);

in the embodiment of the invention, the rootAccording to tables 3 and 4, the associated weight of the commodity "love" purchased by the female user is

wherein ,(V₁(c_x))_iRepresenting a user class c_xPurchased article G_iFrequency weight of (2), i.e. user class c_xFor commodity G_iThe total purchase frequency of (R3 (c)_x))_iIn the user class c_xTotal frequency of purchases for all items

The ratio of (A) to (B);

in the embodiment of the present invention, according to table 3, the frequency weight of the "love" of the commodity purchased by the female user is

(U₁(c_x))_iRepresenting a user class c_xPurchased article G_iValue weight of (1), i.e. user class c_xPurchased article G_iTotal value of (Val3 (c)_x))_iIn the user class c_xTotal value of all purchased goods

The ratio of (A) to (B);

in the embodiment of the present invention, according to table 6, the value weight of the "love" of the commodity purchased by the female user is

Step5, counting the frequency of the simultaneous purchasing of the commodities of each user type according to the commodity transaction data of the user; and creates a directed commodity purchase frequency matrix GG1 (c) for each user category_x)；

Extracting user category c from user commodity transaction data_xBased on the user category c_xCreating a user category c_xThe directional commodity purchase frequency matrix;

commodity G_iAnd G_jN x n matrix GG1 (c) for determining whether there is a directed simultaneous purchase trigger relationship between_x)＝{(R4(c_x))_ijRepresents; (R4 (c)_x))_ijThe value of (D) represents the user class c_xPurchase of goods G in transaction data of_iWhether or not to trigger the purchase of the commodity G_j；(R4(c_x))_ij1 denotes purchase of an article G_iTime triggered purchase of merchandise G_j；(R4(c_x))_ij0 denotes the purchase of the product G_iWithout triggering the purchase of the commodity G_j；i＝{1，2，3...n}，j＝{1，2，3...n}；

In the embodiment of the invention, in the transaction data of which the user category is female, a commodity purchasing frequency matrix is shown in a table 7;

love

Suspense questions of the present invention

Art and literature

Comprehensive art

Swordsman

Movement of

History of

Youth of youth

Crime

War

Cartoon

Love

/

1

2

Suspense questions of the present invention

/

Art and literature

/

1

Comprehensive art

1

2

/

3

1

Swordsman

/

1

Movement of

/

History of

/

Youth of youth

1

/

Crime

1

/

War

/

Cartoon

/

TABLE 7

Step6, according to the directional commodity purchasing frequency moment of each user categoryArray GG1 (c)_x) Creating a undirected merchandise purchase frequency matrix GG2 for each user category (c)_x)；

User class c_xThe undirected commodity purchase frequency matrix of (2) is represented by an n × n matrix GG2 (c)_x)＝{(R5(c_x))_ijRepresents; (R5 (c)_x))_ijThe value of (D) represents the user class c_xIn transaction data of (1) commodity G_iAnd G_jFrequency of concurrent purchases;

commodity G_iAnd G_jThe presence of a simultaneous purchase trigger relationship includes: purchasing goods G_iTime triggered purchase of merchandise G_jAnd purchase of goods G_jTime triggered purchase of merchandise G_iThe relationship of (1);

in the embodiment of the invention, in the transaction data of which the user category is female, the purchase frequency matrix of the undivided commodities is shown in a table 8;

love

Suspense questions of the present invention

Art and literature

Comprehensive art

Swordsman

Movement of

History of

Youth of youth

Crime

War

Cartoon

Love

/

1

2

Suspense questions of the present invention

/

Art and literature

1

/

3

Comprehensive art

2

3

/

4

1

Swordsman

/

1

Movement of

1

/

History of

/

Youth of youth

2

4

/

Crime

1

/

War

/

Cartoon

1

/

TABLE 8

Step7, directed commodity purchase frequency matrix GG1 for each user category (c)_x) Normalization processing is carried out to obtain a corresponding directional commodity purchase weighting frequency matrix GG11 (c)_x)；

For user class c_xDirected commodity purchase frequency matrix GG1 (c)_x) The normalized calculation formula is as follows:

wherein ,(F2_x)_ijRepresenting a user class c_xPurchasing goods G_iTime triggered purchase of merchandise G_jThe normalized frequency order value of (1) is also called a weighted frequency order value; (R4 (c)_x))_ijRepresenting a user class c_xPurchasing goods G_iTime triggered purchase of merchandise G_jThe frequency of (2); min (GG1 (c)_x) Indicating a directional commodity purchase frequency matrix GG1 (c)_x) Minimum order of frequencies; max (GG1 (c)_x) A matrix GG1 (c) indicating the frequency of purchasing commodities_x) Maximum frequency order value of;

in the embodiment of the present invention, the directional commodity purchase weighting frequency matrix data in which the user category is girl is shown in table 9;

love

Suspense questions of the present invention

Art and literature

Comprehensive art

Swordsman

Movement of

History of

Youth of youth

Crime

War

Cartoon

Love

/

0.1

0.2

Suspense questions of the present invention

/

Art and literature

/

0.1

Comprehensive art

0.1

0.2

/

0.3

0.1

Swordsman

/

0.1

Movement of

/

History of

/

Youth of youth

0.1

/

Crime

0.1

/

War

/

Cartoon

/

Table 9Step8, calculates the trigger center degree (D) of each commodity for each user category₁(c_x))_i；

wherein ,(D₁(c_x))_iRepresenting a user class c_xPurchased article G_iTriggering centrality of (3); (CF (c)_x))_iRepresenting a user class c_xPurchased article G_iDegree of triggering, i.e. user class c_xPurchasing goods G_iThe normalized frequency of purchasing other commodity types is triggered;

wherein i≠j；

step9, calculating the network core degree (H) of each commodity of each user category₁(c_x))_i；

Fig. 2 is a flowchart of a method for calculating a network core degree of each commodity of each user category according to an embodiment of the present invention, including the following steps:

s91, GG22 (c) according to the undivided commodity purchase frequency matrix_x) Building a commodity connection relation network;

the method specifically comprises the following steps: establishing a commodity connection relation network by taking commodities as nodes, taking the connection relation among the commodities as edges and the frequency of simultaneous purchase among the commodities as edge weights;

if commodity node G_i and G_jThere is a simultaneous purchase triggering relationship (direct connection relationship) between them, the commodity node G_i and G_jA connecting edge is added between the two edges; commodity node G_i and G_jThe intermediate nodes are connected by a path which comprises at least 1 intermediate node and at least 2 connecting edges, and the number of the paths is at least one;

s92, for the commodity node G with direct or indirect connection_i and G_jObtaining a commodity node G_jRelative node G_iNode level lev;

let G_iIs the original node, if the commodity node G_i and G_jThere is a direct connection relationship between them, node G_jIs G_iA level 1 node of (1);

if commodity node G_i and G_jThere is indirect connection relation and connect through m node paths, m is greater than or equal to 1, P ═ path for m node path set₁，path₂，….G_mRepresents; the number of connection edges included in each node path in P is S ═ side₁，side₂，….side_mDenotes, then node G_jRelative node G_iThe node level of (c) lev ═ min(s), min(s) is not less than 2, and min () represents the minimum value;

FIG. 3 shows a commodity node G provided in the embodiment of the present invention₁、G₂、G₃、G₄、G₅A connection relationship network diagram of (1);

wherein G₁ and G₂If there is a direct connection, G₂Is G₁A level 1 node of (1);

G₁ and G₃An indirect connection relation exists, and the first node path and the second node path can be indirectly connected; the first node path includes 2 connecting edges: g₁-G₂ and G₂-G₃(ii) a The second node path includes 4 connecting edges: g₁-G₂、G₂-G₄、G₄-G₅ and G₅-G₃(ii) a Then G is₃Is G₁A level 2 node of (1);

the value on the connecting edge represents the edge weight (i.e., the frequency of purchases at the same time) between two directly connected commodity nodes, such as G₁ and G₂The edge weight of (1) is 10, G₂ and G₃The edge weight of (2) is 5;

s93, calculating the network median degree F of each commodity node of each user category_b(i)；

Wherein, (FB (c)_x))_iRepresenting a user class c_xCommodity node G_iThe median level in the network of (2); lev_ijPresentation and merchandise node G_iDirect or indirect connected commodity node G_jNode level of (2); (R3 (c)_x))_jPresentation and merchandise node G_iDirect or indirect connected commodity node G_jTotal purchase frequency of corresponding goods, i.e. user class c_xFor commodity G_jTotal frequency of purchases; n represents the total number of commodity nodes;

s94, calculating network core degree (H) of each commodity node of each user type₁(c_x))_i；

wherein ,(H₁(c_x))_iRepresenting a user class c_xCommodity node G_iThe network core degree of (c);

step10, associating the weight (W) of each commodity with each user category₁(c_x))_iFrequency weight (V)₁(c_x))_iValue weight (U)₁(c_x))_iTrigger centrality (D)₁(c_x))_iNetwork core degree (H)₁(c_x))_iNormalization processing is carried out to obtain corresponding weighted associated weight (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_i；

The normalized calculation formula is:

wherein, F represents the value after normalization processing; r respectively represents a user category c_xPurchasing goods G_iAssociated weight (W)₁(c_x))_iFrequency weight (V)₁(c_x))_iValue weight (U)₁(c_x))_iTrigger centrality (D)₁(c_x))_iNetwork core degree (H)₁(c_x))_i(ii) a min (T) respectively represents the minimum value in the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree data of all the commodities; max (T) respectively represents the maximum numerical value in the data of the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree of all the commodities;

for example, if F represents normalized network core, then R represents user class c_xCommodity node G_iNetwork core ofDegree (H)₁(c_x))_i(ii) a min represents user class c_x(ii) a network core degree set of all commodity nodes { (H)₁(c_x))_iI | i | 1,2,3 … … n } and max represents the user category c_x(ii) a network core degree set of all commodity nodes { (H)₁(c_x))_iI | i ═ 1,2,3 … … n } maximum; the rest is analogized in the same way;

step11, weight associating weight (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i；

(S(c_x))_i＝((W₂(c_x))_i+(V₂(c_x))_i+(U₂(c_x))_i+(D₂(c_x))_i+(H₂(c_x))_i)/5；

Wherein (S (c)_x))_iRepresenting a user class c_xCommodity node G_iThe optimal node degree of (2);

alternatively,

calculating a weighted relevance weight (W) from an analytic hierarchy process₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeights in the goods recommendation decision target are weighted and associated with weights (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i；

(S(c_x))_i＝α₁(W₂(c_x))_i+α₂(V₂(c_x))_i+α₃(U₂(c_x))_i+α₄(D₂(c_x))_i+α₅(H₂(c_x))_i；

wherein ,α₁、α₂、α₃、α₄、α₅Respectively represent (W)₂(c_x))_i、(V₂(c_x))_i、(U₂(c_x))_i、(D₂(c_x))_i、(H₂(c_x))_iWeights in the goods recommendation decision target;

specifically, the commodity recommendation is used as a decision target, and the weight (W) is weighted and associated₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iThe evaluation matrix is used as a judgment matrix element in an analytic hierarchy process, and the importance degree of the element is subjected to pairwise comparison evaluation; the analytic hierarchy process is a conventional process, and is described herein again;

specifically, in an application scenario, the judgment is performed according to specific services, and specific evaluation filling of the judgment model is shown in table 10;

watch 10

Weighted relevance weights (W) calculated from analytic hierarchy process₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeight α of₁、α₂、α₃、α₄、α₅Respectively as follows: 0.55, 023, 0.08, 0.08, 0.08;

the best node degree of the user' S goods is classified (S (c)_x))_iComprises the following steps:

(S(c_x))_i＝0.55(W₂(c_x))_i+0.23(V₂(c_x))_i+0.08(U₂(c_x))_i+0.08(D₂(c_x))_i+0.08(H₂(c_x))_i；

and Step12, recommending the commodities to the corresponding classified users according to the optimal node degree of the commodities of the classified users.

Specifically, in the embodiment, the commodities are sorted according to the size of the optimal node degree of the commodities, and the commodities with the large optimal node degree are selected to be preferentially pushed to corresponding classified users;

corresponding to the above-mentioned commodity recommendation method based on user attributes and commodity types, fig. 4 is a structure diagram of a commodity recommendation system based on user attributes and commodity types according to an embodiment of the present invention; the system comprises:

Further, the user commodity transaction data includes fields: user number, user category, commodity category and transaction time;

further, in the embodiment of the present invention, it is assumed that the commodity platform has n kinds of commodities in total, and the set G ═ G is used₁，G₂，….G_nRepresents; n represents the number of commodity types, also called the number of commodity nodes; there are a total of k user categories, with the set C ═ C₁，c₂，….c_kDenotes, i ═ 1,2, 3.. n }, and x ═ 1,2, 3.. k };

further, user category c_xPurchased article G_iThe total value of (a) is determined by k × n matrix CF4 { (Val3 (c)_x))_iDenotes (Val3 (c)_x))_iThe value of (D) represents the user class c_xPurchased article G_iThe total value of (c); (Val3 (c)_x))_i＝(R3(c_x))_i×(Pr)_i；

Further, the calculating of the association weight, the frequency weight, and the value weight of each commodity of each user category specifically includes:

The ratio of (A) to (B);

The ratio of (A) to (B);

further, according to the user commodity transaction data, counting the frequency of the commodities of each user category which are purchased simultaneously; and creates a directed commodity purchase frequency matrix GG1 (c) for each user category_x) The method specifically comprises the following steps:

Further, the directional commodity purchase frequency matrix GG1 (c) according to the user categories_x) Creating a undirected merchandise purchase frequency matrix GG2 for each user category (c)_x) The method specifically comprises the following steps:

further, the directional commodity purchase frequency matrix GG1 for each user category (c)_x) Normalization processing is carried out to obtain a corresponding directional commodity purchase weighting frequency matrix GG11 (c)_x) The method specifically comprises the following steps:

further, the triggering centrality (D) of each commodity of each user category is calculated₁(c_x))_iThe method specifically comprises the following steps:

wherein i≠j；

further, fig. 5 is a structural diagram of a network core degree computing device according to an embodiment of the present invention; the network core degree calculating device comprises:

a commodity connection relation network construction module used for GG22 (c) according to the undivided commodity purchasing frequency matrix_x) Building a commodity connection relation network;

a node grade acquisition module for the commodity node G with direct or indirect connection_i and G_jObtaining a commodity node G_jRelative node G_iNode level lev;

a network median calculation module for calculating network median F of each commodity node of each user category_b(i)；

a network core degree calculation module for calculating the network core degree (H) of each commodity node of each user category₁(c_x))_i；

wherein ,(H₁(c_x))_iRepresenting a user class c_xCommodity node G_iNetwork core degree of (2).

Further, the user categories are associated with the commodities by weight (W)₁(c_x))_iFrequency weight (V)₁(c_x))_iValue weight (U)₁(c_x))_iTrigger centrality (D)₁(c_x))_iNetwork core degree (H)₁(c_x))_iNormalization processing is carried out to obtain corresponding weighted associated weight (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_i；

The normalized calculation formula is:

wherein, F represents the value after normalization processing; r respectively represents a user category c_xPurchasing goods G_iAssociated weight (W)₁(c_x))_iFrequency weight (V)₁(c_x))_iValue weight (U)₁(c_x))_iTrigger centrality (D)₁(c_x))_iNetwork core degree (H)₁(c_x))_i(ii) a min (T) represents the associated weight, frequency weight, value weight, triggering centrality and network core degree of all the commoditiesA minimum value of (d); max (T) respectively represents the maximum numerical value in the data of the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree of all the commodities;

for example, if F represents normalized network core, then R represents user class c_xCommodity node G_iNetwork core degree (H)₁(c_x))_i(ii) a min represents user class c_x(ii) a network core degree set of all commodity nodes { (H)₁(c_x))_iI | i | 1,2,3 … … n } and max represents the user category c_x(ii) a network core degree set of all commodity nodes { (H)₁(c_x))_iI | i ═ 1,2,3 … … n } maximum; the rest is analogized in the same way;

further, the weight (W) is weighted and associated₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i(ii) a The method specifically comprises the following steps:

further, the "will weight the associated weight (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i"can be replaced by:

"computing weighted correlations according to analytic hierarchy ProcessWeight (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeights in the goods recommendation decision target are weighted and associated with weights (W)₂(c_x))_iWeighted frequency weight (V)₂(c_x))_iWeighted value weight (U)₂(c_x))_iWeighted trigger centrality (D)₂(c_x))_iWeighted network core degree (H)₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i；”；

(S(c_x))_i＝α₁(W₂(c_x))_i+α₂(V₂(c_x))_i+α₃(U₂(c_x))_i+α₄(D₂(c_x))_i+α₅(H₂(c_x))_i wherein ,α₁、α₂、α₃、α₄、α₅Respectively represent (W)₂(c_x))_i、(V₂(c_x))_i、(U₂(c_x))_i、(D₂(c_x))_i、(H₂(c_x))_iWeights in the goods recommendation decision target;

watch 10

further, the commodity recommendation is performed on the corresponding classified users according to the optimal node degrees of the commodities of the classified users, specifically in the embodiment, the commodities are sorted according to the optimal node degrees of the commodities, and the commodities with the high optimal node degrees are selected to be preferentially pushed to the corresponding classified users;

an embodiment of the present invention further provides a terminal device, where the terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in one embodiment of the merchandise recommendation method based on the user attribute and the merchandise type, such as steps 1 to Step12 shown in fig. 1. Or, the processor, when executing the computer program, implements the functions of the modules in the above-mentioned product recommendation system embodiment based on the user attribute and the product type.

It will be understood by those skilled in the art that all or part of the steps in the method according to the above embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, such as ROM, RAM, magnetic disk, optical disk, etc.

The sequence number of each step in the foregoing embodiments does not mean the execution sequence, and the execution sequence of each process should be determined by the function and the internal logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims

1. A commodity recommendation method based on user attributes and commodity types is characterized by comprising the following steps:

calculating the triggering centrality of each commodity of each user category;

calculating the network core degree of each commodity of each user category;

2. The commodity recommendation method based on the user attributes and the commodity types according to claim 1, wherein the association weight, the frequency weight, the value weight, the triggering centrality and the network centrality of each commodity of each user category are normalized to obtain the corresponding weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network centrality, instead of:

and calculating the weights of the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree in the commodity recommendation decision target according to an analytic hierarchy process, and performing weighted summation on the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree to obtain the optimal node degree for classifying the user commodities.

3. The commodity recommendation method based on user attributes and commodity types according to claim 2, wherein said user commodity transaction data comprises fields of: user number, user category, commodity category and transaction time; the classification user commodity association information table comprises the following fields: a user number, a user category, and a number of specific merchandise categories.

4. The commodity recommendation method based on the user attributes and the commodity types according to claim 3, wherein the total purchase frequency of each commodity by each user category is counted; the method for counting the associated purchase frequency of each user category on each commodity specifically comprises the following steps:

let the commodity platform total n kinds of commodities, use set G ═ G₁，G₂，….G_nRepresents; n represents the number of commodity types, also called the number of commodity nodes; there are a total of k user categories, with the set C ═ C₁，c₂，….c_kDenotes, i ═ 1,2, 3.. n }, and x ═ 1,2, 3.. k };

user class c_xFor commodity G_iIs given by k × n matrix CF2 { (R2 (c)_x))_iDenotes (R2 (c)_x))_iThe value of (D) represents the user class c_xFor commodity G_iAssociated purchase frequency of; commodity G_iThe associated purchase frequency of (2) indicates that the purchased product includes product G_iFrequency of (i.e. article G)_iFrequency of simultaneous purchases with all other merchandise.

5. The item recommendation method based on the user attributes and the item types according to claim 4, wherein the calculating the total value of each item according to the total purchase frequency and the unit price of each item for each user category specifically comprises:

(Val3(c_x))_i＝(R3(c_x))_i×(Pr)_i。

6. the method for recommending commodities based on user attributes and commodity types according to claim 5, wherein the calculating of the association weight, the frequency weight and the value weight of each commodity of each user category specifically comprises:

The ratio of (A) to (B);

The ratio of (a) to (b).

7. The commodity recommendation method based on the user attributes and the commodity types according to claim 6, wherein the normalization processing of the directional commodity purchase frequency matrix of each user category to obtain the corresponding directional commodity purchase weighting frequency matrix specifically comprises:

wherein ,(F2_x)_ijRepresenting a user class c_xPurchasing goods G_iTime triggered purchase of merchandise G_jThe normalized frequency order value of (1) is also called a weighted frequency order value; (R4 (c)_x))_ijRepresenting a user class c_xPurchasing goods G_iTime triggered purchase of merchandise G_jThe frequency of (2); min (GG1 (c)_x) Indicating a directional commodity purchase frequency matrix GG1 (c)_x) Minimum order of frequencies; max (GG1 (c)_x) A matrix GG1 (c) indicating the frequency of purchasing commodities_x) Of the maximum frequency order.

8. The method for recommending commodities based on user attributes and commodity types according to claim 7, wherein said calculating the triggering centrality of each commodity of each user category specifically comprises:

wherein i≠j.

9. The method of claim 8, wherein the calculating the network core degree of each commodity for each user category comprises:

constructing a commodity connection relation network according to the undirected commodity purchasing frequency matrix;

for commodity nodes G with direct or indirect connections_i and G_jObtaining a commodity node G_jRelative node G_iNode level lev;

calculating the network median degree of each commodity node of each user category;

and calculating the network core degree of each commodity node of each user class.

10. The item recommendation method based on the user attributes and the item types according to claim 9,

the purchase frequency matrix GG22 (c) according to the undivided commodities_x) Building a commodity connection relation network, specifically:

establishing a commodity connection relation network by taking commodities as nodes, taking the connection relation among the commodities as edges and the frequency of simultaneous purchase among the commodities as edge weights;

if commodity node G_i and G_jThere is a simultaneous purchase triggering relationship between them, then the commodity node G_i and G_jA connecting edge is added between the two edges; commodity node G_i and G_jThe intermediate nodes are connected by a path which comprises at least 1 intermediate node and at least 2 connecting edges, and the number of the paths is at least one;

the calculation of the network medium level of each commodity node of each user category is specifically as follows:

the method for calculating the network core degree of each commodity node of each user category specifically comprises the following steps:

11. The commodity recommendation method based on user attributes and commodity types according to claim 10, wherein the association weight, the frequency weight, the value weight, the triggering centrality, and the network core degree of each commodity of each user category are normalized respectively,

the normalized calculation formula is:

wherein, F represents the value after normalization processing; r respectively represents a user category c_xPurchasing goods G_iAssociated weight (W)₁(c_x))_iFrequency weight (V)₁(c_x))_iValue weight (U)₁(c_x))_iTrigger centrality (D)₁(c_x))_iNetwork core degree (H)₁(c_x))_i(ii) a min (T) respectively represents the minimum value in the association weight, the frequency weight, the value weight, the triggering centrality and the network core degree data of all the commodities; max (t) represents the maximum value among the association weight, frequency weight, value weight, trigger centrality, and network core data of all the commodities, respectively.

12. The commodity recommendation method based on user attributes and commodity types according to any one of claims 1 or 3 to 11, wherein the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted trigger centrality, and the weighted network core (H) are assigned₂(c_x))_iWeighted summation is carried out to obtain the optimal node degree (S (c)) for classifying the user commodities_x))_i；

Wherein (S (c)_x))_iRepresenting a user class c_xCommodity sectionPoint G_iThe optimal node degree of (c).

13. The commodity recommendation method based on user attributes and commodity types according to any one of claims 1 to 11, wherein weights of the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree in a commodity recommendation decision target are calculated according to an analytic hierarchy process, and the weighted association weight, the weighted frequency weight, the weighted value weight, the weighted triggering centrality and the weighted network core degree are subjected to weighted summation to obtain an optimal node degree for classifying the commodities of the user;

(S(c_x))_i＝α₁(W₂(c_x))_i+α₂(V₂(c_x))_i+α₃(U₂(c_x))_i+α₄(D₂(c_x))_i+α₅(H₂(c_x))_i wherein ,α₁、α₂、α₃、α₄、α₅Respectively represent (W)₂(c_x))_i、(V₂(c_x))_i、(U₂(c_x))_i、(D₂(c_x))_i、(H₂(c_x))_iWeight in a goods recommendation decision target.

14. A merchandise recommendation system based on user attributes and merchandise types, the system comprising:

15. The commodity recommendation system according to claim 14, wherein said network core degree calculating means comprises:

the commodity connection relation network construction module is used for constructing a commodity connection relation network according to the undirected commodity purchasing frequency matrix;

the network median degree calculating module is used for calculating the network median degree of each commodity node of each user category;

and the network core degree calculating module is used for calculating the network core degree of each commodity node of each user type.

16. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the item recommendation method based on user attributes and item types as claimed in any one of claims 1 to 13.