CN108711075A - A kind of Products Show method and apparatus - Google Patents
A kind of Products Show method and apparatus Download PDFInfo
- Publication number
- CN108711075A CN108711075A CN201810492957.0A CN201810492957A CN108711075A CN 108711075 A CN108711075 A CN 108711075A CN 201810492957 A CN201810492957 A CN 201810492957A CN 108711075 A CN108711075 A CN 108711075A
- Authority
- CN
- China
- Prior art keywords
- product
- user
- matrix
- recommended
- target user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Technology Law (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
This specification embodiment provides a kind of Products Show method and apparatus, wherein the method is used to determine whether that by Products Show to be recommended, to target user, this method includes：The associated multi-field information of target user is obtained, which includes：Target user is in the purchase data of the product scope of product to be recommended and the purchase data of other product scopes；According to the user characteristics matrix of multi-field information architecture target user；For a product to be recommended, the user characteristics matrix for the multiple users for buying product to be recommended is obtained, and based on the characteristic value in matrix, obtains the product feature matrix of product to be recommended；Respectively by user characteristics matrix and product feature Input matrix machine learning model, user preference vector and product preference vector are obtained；The selection assessed value between product to be recommended and target user is obtained according to user preference vector and product preference vector；When selecting assessed value to be more than scheduled recommendation threshold value, it is determined that by Products Show to be recommended to target user.
Description
Technical field
This disclosure relates to technical field of data processing, more particularly to a kind of Products Show method and apparatus.
Background technology
In Products Show field, cold start-up and Sparse are common problems.Cold start-up is lacking a large number of users number
Products Show is carried out in the case of according to support；It is only the ice of overall project that Sparse, which is with the project of user's generation interactive relation,
One jiao of mountain, the data for resulting in user items rating matrix are extremely sparse.For example, in the recommendation of finance and money management product, due to
The behavioural information of the attributes such as number of deals is big, the frequency is low possessed by finance and money management industry itself, user is rare, and there is no a large amount of
User data to do Products Show, lead to the problem of cold start-up；Also, the buying behavior of the finance and money management product of user only accounts for
The sub-fraction of total registration user of finance product platform, Sparse Problem are also very prominent.
So far, the personalized recommendation method being most widely used is the collaborative filtering based on single field, i.e., to target
User recommends the product liked with the most similar user of his interest preference, or the most similar with product that he once liked
Product.It it is one to the recommendation results that user one is more satisfied but how in the case of cold start-up and Sparse
Urgent problem to be solved.
Invention content
In view of this, this specification one or more embodiment provides a kind of Products Show method and apparatus, to improve number
Products Show quality in the case of according to lacking.
Specifically, this specification one or more embodiment is achieved by the following technical solution：
In a first aspect, provide a kind of Products Show method, the method be used to determine whether by Products Show to be recommended to
Target user, the method includes：
The associated multi-field information of the target user is obtained, the multi-field information includes：The target user is in institute
State the purchase data of the product scope of product to be recommended and the purchase data of other product scopes；
According to the multi-field information, the user characteristics matrix of the target user, the user characteristics matrix packet are built
It includes：According to multiple characteristic values of the multi-field information quantization；
For a product to be recommended, the user characteristics for the multiple users for buying the product to be recommended are obtained
Matrix, and the characteristic value in the user characteristics matrix based on the multiple user, it is corresponding to obtain the product to be recommended
Product feature matrix；
The machine learning model for respectively training the user characteristics matrix and product feature Input matrix in advance, is used
Family preference vector and product preference vector, the user preference vector are used to indicate preference of the target user in product purchase,
The product preference vector is used to indicate to buy user's feature of the product to be recommended；
According to the user preference vector and product preference vector, obtain the product to be recommended and the target user it
Between selection assessed value, the selection assessed value is for indicating that the target user buys the probability of the product to be recommended；
When the selection assessed value is more than scheduled recommendation threshold value, it is determined that by the Products Show to be recommended to described
Target user.
Second aspect, provides a kind of Products Show device, described device be used to determine whether by Products Show to be recommended to
Target user, described device include：
Data obtaining module, for obtaining the associated multi-field information of the target user, the multi-field information includes：
The target user is in the purchase data of the product scope of the product to be recommended and the purchase data of other product scopes；
User's matrix builds module, for according to the multi-field information, building the user characteristics square of the target user
Battle array, the user characteristics matrix include：According to multiple characteristic values of the multi-field information quantization；
Product matrix builds module, for for a product to be recommended, obtaining and buying the product to be recommended
The user characteristics matrix of multiple users, and the characteristic value in the user characteristics matrix based on the multiple user, obtain
To the corresponding product feature matrix of the product to be recommended；
Model processing modules, the machine for respectively training the user characteristics matrix and product feature Input matrix in advance
Device learning model obtains user preference vector and product preference vector, and the user preference vector is for indicating that target user exists
Preference in product purchase, the product preference vector are used to indicate to buy user's feature of the product to be recommended；
Output processing module, for according to the user preference vector and product preference vector, obtaining the production to be recommended
Selection assessed value between product and the target user, the selection assessed value are waited for for indicating described in target user's purchase
The probability of recommended products；
Recommend determining module, for when the selection assessed value is more than scheduled recommendation threshold value, it is determined that waited for described
Recommended products recommends the target user.
The third aspect provides a kind of Products Show equipment, and the equipment includes memory, processor, and is stored in
On reservoir and the computer instruction that can run on a processor, the processor realize following steps when executing instruction：
The associated multi-field information of the target user is obtained, the multi-field information includes：The target user is in institute
State the purchase data of the product scope of product to be recommended and the purchase data of other product scopes；
According to the multi-field information, the user characteristics matrix of the target user, the user characteristics matrix packet are built
It includes：According to multiple characteristic values of the multi-field information quantization；
For a product to be recommended, the user characteristics for the multiple users for buying the product to be recommended are obtained
Matrix, and the characteristic value in the user characteristics matrix based on the multiple user, it is corresponding to obtain the product to be recommended
Product feature matrix；
The machine learning model for respectively training the user characteristics matrix and product feature Input matrix in advance, is used
Family preference vector and product preference vector, the user preference vector are used to indicate preference of the target user in product purchase,
The product preference vector is used to indicate to buy user's feature of the product to be recommended；
According to the user preference vector and product preference vector, obtain the product to be recommended and the target user it
Between selection assessed value, the selection assessed value is for indicating that the target user buys the probability of the product to be recommended；
When the selection assessed value is more than scheduled recommendation threshold value, it is determined that by the Products Show to be recommended to described
Target user.
The Products Show method and apparatus of this specification one or more embodiment, by the user's row for merging multiple fields
For data and essential information, and relevant preference profiles, side are bought using deep neural network intelligence perception user and product
User is helped to select suitable finance and money management product, it is sparse with cold start-up problem to be effectively relieved transaction data that the sector is faced,
The accuracy for effectively increasing finance and money management personalization of product recommendation, more accurate recommendation service is provided for target user.
Description of the drawings
In order to illustrate more clearly of this specification one or more embodiment or technical solution in the prior art, below will
A brief introduction will be made to the drawings that need to be used in the embodiment or the description of the prior art, it should be apparent that, in being described below
Attached drawing is only some embodiments described in this specification one or more embodiment, and those of ordinary skill in the art are come
It says, without having to pay creative labor, other drawings may also be obtained based on these drawings.
Fig. 1 is the process for the model training that this specification one or more embodiment provides；
Fig. 2 is the attribute interactive operation principle for the eigenmatrix that this specification one or more embodiment provides；
Fig. 3 is the Processing with Neural Network schematic diagram that this specification one or more embodiment provides；
Fig. 4 is a kind of structural schematic diagram for Products Show device that this specification one or more embodiment provides.
Specific implementation mode
In order to make those skilled in the art more fully understand the technical solution in this specification one or more embodiment,
Below in conjunction with the attached drawing in this specification one or more embodiment, to the technology in this specification one or more embodiment
Scheme is clearly and completely described, it is clear that and described embodiment is only this specification a part of the embodiment, rather than
Whole embodiments.Based on this specification one or more embodiment, those of ordinary skill in the art are not making creativeness
The every other embodiment obtained under the premise of labour should all belong to the range of disclosure protection.
This specification one or more embodiment provides a kind of Products Show method when Sparse, and this method is retouched
It states by taking the recommendation of finance and money management product as an example, but it is understood that, this method may be equally applicable for other and be opened with cold
The Products Show scene of dynamic feature.
Wherein, which has merged the user behavior data from different field, by product scope to be recommended
Except other field behavioural information, alleviate the Sparse and cold start-up problem of product scope to be recommended.Because user exists
The buying behavior of other field, which can also react identity characteristic, environmental characteristic, life taste of user etc., to be helped to react user
Product buys the information of preference, and the recommendation for treating recommended products field also has good reference function.
In addition, the recommendation method also uses machine learning model, for example, by taking deep neural network as an example, the depth is utilized
The output result of neural network model is spent to assist carrying out Products Show.Certainly, deep neural network model can first carry out mould
Type training, and the use for the model progress Products Show completed using training.
The training of model：
First, deep neural network model of the training for finance and money management Products Show.
The actual acquired data that can be bought according to product, builds the objective matrix of model training.The actual acquired data
In may include purchase data of the user to product, for example can be user and the actual purchase of finance and money management product is recorded, example
Such as, user A has purchased fund J1, and user B has purchased stock G1 and fund J1, and user C has purchased fund J2, etc..According to above-mentioned
Actual acquired data, objective matrix can be built, such as a kind of objective matrix of the following table 1 example, but not limited to this：
1 objective matrix of table
| Product 1 (fund J1) | Product 2 (stock G1) | Product 3 (fund J2) | |
| User A | 1 | 0 | 0 |
| User B | 1 | 1 | 0 |
| User C | 0 | 0 | 1 |
In above-mentioned table 1, objective matrix may include purchase selective value of the user to product, and the purchase selective value is used for
Indicate whether user buys product.Illustratively, purchase selective value may include " 1 " or " 0 ", when numerical value is 1, indicate to use
Family has purchased the product；When numerical value is 0, indicate that user does not buy the product.The objective matrix can be used as depth nerve net
The training objective of network model, when the deviation between the output result and the training objective of the model in training is smaller and smaller, and
When deviation reaches predetermined threshold, just terminate the training of model, and the model that training terminates is directly used in follow-up finance and money management and is produced
The recommendation of product.
Then, in objective matrix each user (for example, user A, user B) and each product (for example, product 1,
Product 2), the user characteristics matrix of each user can be built respectively, and builds the product feature matrix of each product respectively.And
By the user characteristics matrix and product feature Input matrix of structure machine learning model to be trained, output model output matrix,
The model output matrix includes each purchase selective value exported by the machine learning model.In the model output matrix
When reaching predetermined threshold with the deviation of objective matrix, model training terminates.
As follows by the process of the detailed descriptive model training of Fig. 1, should describe how to build above-mentioned user in the process
Eigenmatrix, product feature matrix, and how by Input matrix model with the process of training pattern.
In step 100, the associated multi-field information of target user is obtained, the multi-field information includes：Target user
In the purchase data of the purchase data and other product scopes of the product scope of product to be recommended.
In this step, target user is the user of product to be recommended, such as, it is desirable to user's A recommended products, but still not
Know to the user A and which product recommended, needs recommendation method through this embodiment to determine the production to recommend to user A
Product, then the user A is properly termed as target user.
It should be noted that in model training, goal user can be the user in objective matrix, these use
Family has occurred that actual buying behavior in fact.And model after subsequent model training is in use, target user
Can be the user that not yet certain products are bought of pending Products Show.
By taking finance and money management product as an example, product scope, that is, finance and money management product of product to be recommended, target user waits at this
The purchase data of the product scope of recommended products, such as may include：User buys the transaction amount of some finance and money management product.
And the purchase data of other product scopes can be with the purchase of right and wrong finance and money management product, for example, it may be purchase clothes, purchase electricity
Rice cooker etc..The purchase data of other product scopes can buy the purchasing price of the other field product, for example, purchase
The clothes bought is 200 yuan, and the electric cooker of purchase is 350 yuan.Regardless of being the product scope or other products of product to be recommended
The purchase data in field are all the data generated by target user to buy.
In addition, multi-field information is also not limited to the purchase data in above-mentioned different product field, can also include other
Information.Several, including but not limited to following information of following example：
For example, the customer attribute information of the target user.The customer attribute information can be the gender of user, the age,
Educational background etc..
For example, purchase data of the association user of the target user in the product scope of product to be recommended.Wherein, target
The association user of user can have friend relation, relationship of transferring accounts etc. with target user.Can be mesh by taking friend relation as an example
The data for the purchase finance and money management product that the good friend of mark user occurred, for example, the good friend user a of user A bought some gold
Melt finance product, and transaction amount is 20,000.
For example, the lend-borrow action data of target user.The lend-borrow action that the lend-borrow action data can be occurred with target user,
The product of some category is borrowed or lent money, and the amount of money borrowed or lent money is how many.
In a step 102, according to the multi-field information, the user characteristics matrix of the target user, the use are built
Family eigenmatrix includes：According to multiple characteristic values of the multi-field information quantization.
In this step, collected data in step 100 can be based on and quantified, characteristic value is converted into.
A kind of form of user characteristics matrix of following 2 example of table：
2 user characteristics matrix of table
As upper table 2 can carry out the coarseness processing of product first before the quantization for carrying out characteristic value.At coarseness
Reason is that the data that will more be refined in data set are converted into generality, the higher data of synthesis degree.If for a product category
Purchase data, the product quantity bought under the product category reaches coarseness treatment conditions, then by the product category
Under multiple products carry out coarseness processing.As an example it is assumed that the purchase data of other product scopes include purchase clothes,
Multiple thinner categories such as electric cooker, also, target user just has purchased universal love and thinks DFB-B in electric cooker this category
The processing of the low capacities electric cookers such as 0.8L, oaks AR-Y0801, Lip river shellfish LBF-091BM is the mini electric cooker of 0-1L non-computers, beautiful
A variety of electric cookers such as the domestic full-automatic intelligent electric cooker such as MB-WHS30C96, rice man pressure IH, Panasonic SR-AE101-K.That
If when user characteristics matrix is built, these products are divided it is very thin, for example, other product buying behaviors in table 2
In, including many products such as product 1, product 2, product 3, such as above-mentioned universal love think DFB-B 0.8L, oaks AR-Y0801,
Multiple products such as Lip river shellfish LBF-091BM, then prodigious calculating pressure will be caused.Therefore, coarseness processing can be by granularity water
Equal the granular level that thinner characteristic dimension is aggregated into a relative coarseness.
For example, universal love thinks the processing of the low capacities electric cookers such as DFB-B 0.8L, oaks AR-Y0801, Lip river shellfish LBF-091BM
For the mini electric cooker of 0-1L non-computers, beautiful MB-WHS30C96, rice man pressure IH, Panasonic SR-AE101-K etc. are domestic full-automatic
Intelligent electric cooker processing is 3L-4L intelligent microcomputer electric cookers.And whether coarse grain is carried out to the purchase data of a product category
Degree processing, can be arranged coarseness treatment conditions.For example, the condition can be the product quantity bought under the product category
Reach certain amount threshold, for example, the product quantity under the same product category has reached 6 or more.And in table 2
Customer attribute information, social networks, the purchase of finance and money management product and the characteristic dimensions such as lend-borrow action, due to its characteristic dimension
Less, information content is high, can not have to carry out coarseness processing.
The following characteristic value quantization for illustrating how to carry out each dimension respectively, wherein it should be noted that following quantization
Method is only example, is not limited thereto in actual implementation, can be executed according to other quantitative criterias：
1) the finance and money management product buying behavior of social networks user is established for target user and with target user：
For example, can be multiple areas by transaction amount classifying rationally according to the transaction amount of the finance and money management product of purchase
Between, such as the " < in table 2;Multiple sections such as P1 ", " P1-P2 ", " P2-P3 ".If user buys the amount of money of the finance and money management product
In the section, then 1 is labeled as；Otherwise it is 0.
Wherein, the social networks column in table 2 with target user there is the user of incidence relation to buy finance and money management product
Purchase data will first can establish the institute of social networks since the user of incidence relation may be multiple users with target user
There is the transaction amount of user to be averaged, is marked according to the amount of money of average value.For example, if average value is in section " P1-
P2 ", then can be in the characteristic value label 1 in the corresponding section.
2) for the buying behavior of other products：
As described above, the purchase data of other products have carried out coarseness processing, are in same coarser particle size level
There can be multiple products, and there can be relatively large difference in the price of these products.At this time can with price this
Index buys frequency totality 0-1 standards by each attribute section of all product classifying rationallies under the category to table 2, and by user
For value after change as its attribute value, reaction target user buys the frequent degree of product in the price range under the category.
For example：Assuming that target user is in the buying behavior of other products, 3L-4L intelligent microcomputers electric cooker this
Under one category, has purchased universal love and think at the low capacities electric cookers such as DFB-B 0.8L, oaks AR-Y0801, Lip river shellfish LBF-091BM
Reason is the mini electric cooker of 0-1L non-computers, and the households such as beautiful MB-WHS30C96, rice man pressure IH, Panasonic SR-AE101-K are complete certainly
Intelligent electric cooker is moved, i.e., has purchased multiple product under same category.It can so be looked into according to the respective purchasing price of these products
It sees in " <The product quantity bought in the sections P1 ", and using the quantity as the characteristic value in the corresponding section.For example, described
"<3 products are had purchased in the sections P1 ", then characteristic value is 3；1 product is had purchased in described section " P1-P2 ", then it is right
Should the characteristic value in section can be 1.
3) for the lend-borrow action of target user：
For example, the quantization of the lend-borrow action is similar with the quantization of finance and money management product, equally it is rationally to draw credit amount
It is divided into multiple sections, if the amount of money that user borrows or lends money the category product is in the section, is labeled as 1；Otherwise it is 0.
4) for the essential information of user：
For example, for numeric type variable, such as age can be divided according to method identical with transaction amount.Example
Property, 18 years old~25 years old correspondence, one quantized value, 26 years old~35 years old correspondence, one quantized value.
For example, for classification type variable, such as gender, educational background, then marked after can encoding Variable Factors.Illustratively,
Undergraduate course educational background can correspond to a quantized value, and postgraduate's educational background can correspond to a quantized value.
At step 104, for multiple products, the user characteristics square for the multiple users for buying the product is obtained
Battle array, and the characteristic value in the user characteristics matrix based on the multiple user, obtain the product feature matrix of the product.
Product in this step is finance and money management product.This step can build product feature matrix, a product feature
Matrix can correspond to a product, which can be each product in objective matrix.Wherein, the structure of product feature matrix
It can be based on user characteristics matrix.
For example, by taking a product as an example, that buys the finance and money management product has multiple users, each user constructs
User characteristics matrix shown in table 2.The corresponding multiple user characteristics matrixes of multiple users can be so based on, by characteristic value
It is weighted average.
For example, by taking the age in essential information as an example, buying each user of the product, there are one the spies at corresponding age
The characteristic value of multiple users can be weighted averagely, obtain an age corresponding comprehensive characteristics value by value indicative.
For another example, by taking the category 1 in other product buying behaviors in table 2 as an example, each user in multiple users
There are one the characteristic values of the corresponding category 1, can be weighted the characteristic value of multiple users averagely, it is right to obtain a category 1
The comprehensive characteristics value answered.
It can further be seen that each characteristic value in table 2 corresponds to different characteristic value positions, for example, the x1 in table 2 is corresponded to
Characteristic value position Shi [The corresponding section " P1-P2 " of row, arranges corresponding " 1 " ] of category;, and the corresponding characteristic value positions characteristic value x2 are
[The corresponding section " P2-P3 " of row, arranges corresponding " 1 " ] of category;.It, can be special by the user of multiple users when building product feature matrix
The characteristic value that same characteristic value position is corresponded in sign matrix is weighted averagely, obtains corresponding to the feature in product feature matrix
It is worth the characteristic value of position.
That is, the characteristic value of multiple users, can be weighted averagely, finally obtaining can be anti-by each row in table 2
The product feature matrix of user's global feature of the product should be gone out to buy.
Wherein, the setting of weight when characteristic value weighted average can be determined according to practical business situation.If for example, recognizing
It is more important when reacting user's global feature for the characteristic value of some user, just its weight is arranged some higher.
In step 106, attribute interactive operation is carried out to user characteristics matrix and product feature matrix respectively.
The attribute interactive operation of user characteristics matrix and product feature matrix can be carried out in this step.Attribute interactive operation
It is to establish correlativity between the attribute that will be not directly relevant in matrix, it is first that the eigenmatrix of structure is random as unit of attribute column
Sequence generates multiple new eigenmatrixes, then multiple new eigenmatrix splicings are generated the eigenmatrix after attribute interaction.It needs
It is noted that the attribute interactive operation can be that an optional operation can be more effective after executing attribute interactive operation
It was found that the potential association between different characteristic, thus also can be more accurate when later use machine learning model perceives user preference
Really.
The principle of the attribute interactive operation of eigenmatrix may refer to shown in Fig. 2：
As described in Figure 2, each feature such as feature 1 therein, feature 2, feature 3 corresponds to different characteristic series.With user
For eigenmatrix, feature 1 can be " product 1 in the buying behavior of finance and money management product " in table 1, and feature 15 can be
" the debt-credit category 1 in lend-borrow action " in table 1, i.e., different features corresponds to different lines.According to Fig.2, be equivalent to by
Random movement has been carried out between different lines in table 1, is that unit progress is randomly ordered with row, is then spliced.
In step 108, respectively by the user characteristics matrix and product feature Input matrix machine learning model after interaction,
Obtain user preference vector and product preference vector.
In this step, deep neural network includes two parallel neural networks, one of them is user behavior preference
Intellisense device, the other is buying the Intellisense device of user's general characteristic preference of the product, as shown in Figure 3.By attribute
Interaction and input of the spliced eigenmatrix as parallel neural network, for example, the user characteristics matrix after attribute interaction is defeated
Enter a neural network, product feature Input matrix another neural network after attribute interaction.
After the convolutional layer of neural network, pond layer and full linked operation, it is inclined that neural network can respectively obtain user
Good vector sum product preference vector.Wherein, the user preference vector can be used to indicate that preference of the user in product purchase,
It is equivalent to and indicates a user likes which type of product bought.And the product preference vector can be used to indicate that purchase product
What user is user's feature of the corresponding product of eigenmatrix have the characteristics that is, indicating for a product
It is more likely to buy the product.
In step 110, the user preference vector and product preference vector exported according to model obtains model output square
Battle array, the model output matrix includes each purchase selective value exported by machine learning model.
For example, by the corresponding user characteristics Input matrix neural network model of a user, user preference vector is obtained；It will
The corresponding product feature Input matrix neural network model of one product, obtains product preference vector.It can be inclined according to the user
Good vector sum product preference vector obtains a purchase selective value.For example, can be inclined by above-mentioned user preference vector and product
Good vector seeks inner product, obtains purchase selective value, and the selection value indicates that above-mentioned user buys the probability of the product.
One user characteristics matrix can be built for each user in objective matrix, it can for each product
Corresponding product feature matrix is built respectively.According to above-mentioned method, product of one of user couple can be obtained
Buy selective value.These purchase selective values may be constructed model output matrix, i.e., each purchase that the model output matrix includes
Buy the numerical value that selective value is neural network model output.
And the user that objective matrix includes is to the purchase selective value of product, is obtained according to actual acquired data, is to use
The buying behavior that family actually occurs, objective matrix are the mutual selection matrixs of the user really occurred and product.It can be by target
Training objective of the matrix as neural network model, with continuing to optimize for model, the output result and reality of neural network model
The generation numerical value on border will be closer.
In step 112, when the deviation of the model output matrix and objective matrix reaches predetermined threshold, model training
Terminate.
For example, when can reach predetermined threshold with the deviation of setting model output matrix and objective matrix, terminate the instruction of model
Practice.It can be that deviation is less than or equal to scheduled threshold value that the deviation, which reaches predetermined threshold, i.e., deviation foot between the two
It is enough small.Wherein, the measurement of the deviation of model output matrix and objective matrix can be there are many method, for example, weigh can be with for deviation
Use root-mean-square error RMSE (Root Mean Square Error) or mean absolute error MAE (Mean Absolute
Deviation).It is mutual between prediction user and product according to trained neural network model after model training
When select probability, it will being approached with actual conditions for prediction has the prodigious prediction probability of success.
Use to the model that training terminates：
Assuming that terminating two neural metwork trainings arranged side by side, trained as follows with an example to illustrate how to use
Good model judges to recommend which kind of product that will have higher success rate to user.
For example, it is assumed that include to user's Y recommendation finance and money management products, product to be recommended currently：Products C 1, product
Multiple products such as C2, products C 3, then to recommend which finance and money management product that can have higher success rate to user Y, it can be according to this
The recommendation method of example executes.
The user characteristics matrix of user Y can be first built, and builds multiple productions such as products C 1, products C 2, products C 3 respectively
The product feature matrix of product.Then, the user characteristics matrix of user Y and the product feature matrix of products C 1 are inputted parallel respectively
Neural network, obtain user preference vector and product preference vector.And user Y is obtained to products C 1 based on the two vectors
Assessed value, the selection assessed value is selected to be used to indicate the probability that target user buys assessment product.The selection assessed value with it is upper
The calculation for stating the purchase selective value mentioned is identical, and it is to distinguish only to use two titles, and purchase selective value is in mould
The numerical value calculated when type training, select assessed value be the numerical value calculated in the complete use of model training, for as whether to
The foundation of user's recommended products.
Multiple products such as above-mentioned products C 1 to be recommended, products C 2, products C 3 are properly termed as assessment product, that is, assess these
Whether product will recommend user Y.It can each divide between the product feature matrix of product and the user characteristics matrix of user Y
A selection assessed value is not obtained.A recommendation threshold value can be set, is more than scheduled recommendation threshold value in the selection assessed value
When, it is determined that give the assessment Products Show to the target user.As an example it is assumed that the selection of products C 1 and user Y is commented
Valuation is 0.6, and the selection assessed value of products C 2 and user Y are 0.8, and the selection assessed value of products C 3 and user Y are 0.2, and false
If it is 0.55 to recommend threshold value, then can really directional user Y recommended products C1 and products C 2, not recommended products C3.
The personalized recommendation method of the finance and money management product of this example, by merge multiple fields user behavior data with
Essential information, and relevant preference profiles are bought with product using deep neural network intelligence perception user, help user to choose
Suitable finance and money management product is selected, it is sparse with cold start-up problem to be effectively relieved transaction data that the sector is faced, effectively improves
The accuracy that finance and money management personalization of product is recommended, for target user provides more accurate recommendation service, becomes promotion and sells
The effective measure of good interaction between platform and user.
In order to realize that the above method, at least one embodiment of this specification additionally provide a kind of Products Show device.Such as Fig. 4
Shown, which can be used to determine whether that by Products Show to be recommended, to target user, which may include：Acquisition of information
Module 41, user's matrix structure module 42, product matrix structure module 43, model processing modules 44,45 and of output processing module
Recommend determining module 46.
Data obtaining module 41, for obtaining the associated multi-field information of the target user, the multi-field packet
It includes：The target user is in the purchase data of the product scope of the product to be recommended and the purchase data of other product scopes；
User's matrix builds module 42, for according to the multi-field information, building the user characteristics of the target user
Matrix, the user characteristics matrix include：According to multiple characteristic values of the multi-field information quantization；
Product matrix builds module 43, for for a product to be recommended, obtaining and buying the product to be recommended
Multiple users the user characteristics matrix, and the characteristic value in the user characteristics matrix based on the multiple user,
Obtain the corresponding product feature matrix of the product to be recommended；
Model processing modules 44, for respectively by the training in advance of the user characteristics matrix and product feature Input matrix
Machine learning model obtains user preference vector and product preference vector, and the user preference vector is for indicating target user
Preference in product purchase, the product preference vector are used to indicate to buy user's feature of the product to be recommended；
Output processing module 45, for according to the user preference vector and product preference vector, obtaining described to be recommended
Selection assessed value between product and the target user, the selection assessed value is for indicating described in target user's purchase
The probability of product to be recommended；
Recommend determining module 46, for when the selection assessed value is more than scheduled recommendation threshold value, it is determined that will be described
Products Show to be recommended gives the target user.
In one example, user's matrix builds module 42, is additionally operable to：If for the purchase data of a product category,
The product quantity bought under the product category reaches coarseness treatment conditions, then by multiple products under the product category
Carry out coarseness processing.
In one example, product matrix builds module 43, is specifically used for the user characteristics matrix to the multiple user
The characteristic value of the middle same characteristic value position of correspondence is weighted averagely, obtains corresponding to the feature in the product feature matrix
It is worth the characteristic value of position.
In one example, model processing modules 44 are additionally operable to respectively by the user characteristics matrix and product feature
Before the machine learning model that Input matrix is trained in advance, the user characteristics matrix and product feature matrix are belonged to respectively
Sexual intercourse interoperates；By the user characteristics matrix and product feature matrix after interaction, the machine learning model is inputted.
The device or module that above-described embodiment illustrates can specifically realize by computer chip or entity, or by having
The product of certain function is realized.A kind of typically to realize that equipment is computer, the concrete form of computer can be personal meter
Calculation machine, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media player, navigation are set
It is arbitrary several in standby, E-mail receiver/send equipment, game console, tablet computer, wearable device or these equipment
The combination of equipment.
For convenience of description, it is divided into various modules when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each module is realized can in the same or multiple software and or hardware when specification one or more embodiment.
Each step in above-mentioned flow as shown in the figure, execution sequence are not limited to the sequence in flow chart.In addition, each
The description of a step can be implemented as software, hardware or its form combined, for example, those skilled in the art can be by it
It is embodied as the form of software code, can is the computer executable instructions that can realize the corresponding logic function of the step.
When it is realized in the form of software, the executable instruction can store in memory, and by the processor in equipment
It executes.
For example, corresponding to the above method, this specification one or more embodiment provides a kind of Products Show equipment simultaneously,
The equipment may include processor, memory and storage on a memory and the computer instruction that can run on a processor,
The processor is by executing described instruction, for realizing following steps：
The associated multi-field information of the target user is obtained, the multi-field information includes：The target user is in institute
State the purchase data of the product scope of product to be recommended and the purchase data of other product scopes；
According to the multi-field information, the user characteristics matrix of the target user, the user characteristics matrix packet are built
It includes：According to multiple characteristic values of the multi-field information quantization；
For a product to be recommended, the user characteristics for the multiple users for buying the product to be recommended are obtained
Matrix, and the characteristic value in the user characteristics matrix based on the multiple user, it is corresponding to obtain the product to be recommended
Product feature matrix；
The machine learning model for respectively training the user characteristics matrix and product feature Input matrix in advance, is used
Family preference vector and product preference vector, the user preference vector are used to indicate preference of the target user in product purchase,
The product preference vector is used to indicate to buy user's feature of the product to be recommended；
According to the user preference vector and product preference vector, obtain the product to be recommended and the target user it
Between selection assessed value, the selection assessed value is for indicating that the target user buys the probability of the product to be recommended；
When the selection assessed value is more than scheduled recommendation threshold value, it is determined that by the Products Show to be recommended to described
Target user.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, method, commodity or equipment.
It will be understood by those skilled in the art that this specification one or more embodiment can be provided as method, system or calculating
Machine program product.Therefore, this specification one or more embodiment can be used complete hardware embodiment, complete software embodiment or
The form of embodiment combining software and hardware aspects.Moreover, this specification one or more embodiment can be used at one or
It is multiple wherein include computer usable program code computer-usable storage medium (include but not limited to magnetic disk storage,
CD-ROM, optical memory etc.) on the form of computer program product implemented.
This specification one or more embodiment can computer executable instructions it is general on
Described in hereafter, such as program module.Usually, program module includes executing particular task or realization particular abstract data type
Routine, program, object, component, data structure etc..Can also put into practice in a distributed computing environment this specification one or
Multiple embodiments, in these distributed computing environments, by being executed by the connected remote processing devices of communication network
Task.In a distributed computing environment, the local and remote computer that program module can be located at including storage device is deposited
In storage media.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.At data
For managing apparatus embodiments, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to side
The part of method embodiment illustrates.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the action recorded in detail in the claims or step can be come according to different from the sequence in embodiment
It executes and desired result still may be implemented.In addition, the process described in the accompanying drawings not necessarily require show it is specific suitable
Sequence or consecutive order could realize desired result.In some embodiments, multitasking and parallel processing be also can
With or it may be advantageous.
The foregoing is merely the preferred embodiments of this specification one or more embodiment, not limiting this theory
Bright book one or more embodiment, all within the spirit and principle of this specification one or more embodiment, that is done is any
Modification, equivalent replacement, improvement etc. should be included within the scope of the protection of this specification one or more embodiment.
Claims (11)
1. a kind of Products Show method, the method is used to determine whether Products Show to be recommended to target user, the side
Method includes：
The associated multi-field information of the target user is obtained, the multi-field information includes：The target user waits for described
The purchase data of the product scope of recommended products and the purchase data of other product scopes；
According to the multi-field information, the user characteristics matrix of the target user is built, the user characteristics matrix includes：Root
According to multiple characteristic values of the multi-field information quantization；
For a product to be recommended, the user characteristics square for the multiple users for buying the product to be recommended is obtained
Battle array, and the characteristic value in the user characteristics matrix based on the multiple user, obtain the corresponding production of the product to be recommended
Product eigenmatrix；
The machine learning model for respectively training the user characteristics matrix and product feature Input matrix in advance, it is inclined to obtain user
Good vector sum product preference vector, the user preference vector is used to indicate preference of the target user in product purchase, described
Product preference vector is used to indicate to buy user's feature of the product to be recommended；
According to the user preference vector and product preference vector, obtain between the product to be recommended and the target user
Assessed value is selected, the selection assessed value is for indicating that the target user buys the probability of the product to be recommended；
When the selection assessed value is more than scheduled recommendation threshold value, it is determined that give the Products Show to be recommended to the target
User.
2. according to the method described in claim 1, the multi-field information, further includes at least one of following：
Purchase data of the association user of the target user in the product scope of product to be recommended；
The customer attribute information of the target user.
3. according to the method described in claim 1, it is described structure target user user characteristics matrix, including：
If for the purchase data of a product category, the product quantity bought under the product category reaches coarseness processing
Multiple products under the product category are then carried out coarseness processing by condition.
4. according to the method described in claim 1, the characteristic value in the user characteristics matrix based on the multiple user, obtains
To the corresponding product feature matrix of the product to be recommended, including：
To corresponding to the characteristic value of same characteristic value position in the user characteristics matrix of the multiple user, it is weighted averagely, obtains
To the characteristic value for corresponding to the characteristic value position in the product feature matrix.
5. according to the method described in claim 1, described respectively by the user characteristics matrix and product feature Input matrix
In advance before trained machine learning model, the method further includes：
The machine learning model is trained, training process includes following processing：
According to the actual acquired data that product is bought, the objective matrix of model training is built, the actual acquired data includes using
Family includes to the purchase data of product, the objective matrix：The user determined according to the purchase data selects the purchase of product
Value is selected, the purchase selective value is for indicating whether user buys product；
To each user in the objective matrix, the user characteristics matrix of each user is built respectively；
To each product in the objective matrix, the product feature matrix of each product is built respectively；
By the user characteristics matrix and product feature Input matrix of the structure machine learning model to be trained, and according to model
The user preference vector and product preference vector of output, obtain model output matrix, and the model output matrix includes passing through institute
State each purchase selective value of machine learning model output；
When the deviation of the model output matrix and objective matrix reaches predetermined threshold, model training terminates.
6. according to the method described in claim 1, described respectively that the user characteristics matrix and product feature Input matrix is pre-
First before trained machine learning model, the method further includes：
Attribute interactive operation is carried out to the user characteristics matrix and product feature matrix respectively；
By the user characteristics matrix and product feature matrix after interaction, the machine learning model is inputted.
7. a kind of Products Show device, described device is used to determine whether Products Show to be recommended to target user, the dress
Set including：
Data obtaining module, for obtaining the associated multi-field information of the target user, the multi-field information includes：It is described
Target user is in the purchase data of the product scope of the product to be recommended and the purchase data of other product scopes；
User's matrix builds module, for according to the multi-field information, building the user characteristics matrix of the target user, institute
Stating user characteristics matrix includes：According to multiple characteristic values of the multi-field information quantization；
Product matrix builds module, for for a product to be recommended, obtaining and buying the multiple of the product to be recommended
The user characteristics matrix of user, and the characteristic value in the user characteristics matrix based on the multiple user, obtain institute
State the corresponding product feature matrix of product to be recommended；
Model processing modules, the engineering for respectively training the user characteristics matrix and product feature Input matrix in advance
Model is practised, obtains user preference vector and product preference vector, the user preference vector is for indicating target user in product
Preference in purchase, the product preference vector are used to indicate to buy user's feature of the product to be recommended；
Output processing module, for according to the user preference vector and product preference vector, obtain the product to be recommended and
Selection assessed value between the target user, the selection assessed value are described to be recommended for indicating target user's purchase
The probability of product；
Recommend determining module, for when the selection assessed value is more than scheduled recommendation threshold value, it is determined that will be described to be recommended
Products Show gives the target user.
8. device according to claim 7,
User's matrix builds module, is additionally operable to：If for the purchase data of a product category, under the product category
The product quantity of purchase reaches coarseness treatment conditions, then multiple products under the product category is carried out coarseness processing.
9. device according to claim 7,
The product matrix builds module, is specifically used for corresponding to same characteristic value in the user characteristics matrix of the multiple user
The characteristic value of position is weighted averagely, obtains the characteristic value for corresponding to the characteristic value position in the product feature matrix.
10. device according to claim 7,
The model processing modules are additionally operable to respectively training the user characteristics matrix and product feature Input matrix in advance
Machine learning model before, attribute interactive operation is carried out to the user characteristics matrix and product feature matrix respectively；It will hand over
User characteristics matrix after mutually and product feature matrix, input the machine learning model.
11. a kind of Products Show equipment, the equipment includes memory, processor, and stores on a memory and can locate
The computer instruction run on reason device, the processor realize following steps when executing instruction：
The associated multi-field information of the target user is obtained, the multi-field information includes：The target user waits for described
The purchase data of the product scope of recommended products and the purchase data of other product scopes；
According to the multi-field information, the user characteristics matrix of the target user is built, the user characteristics matrix includes：Root
According to multiple characteristic values of the multi-field information quantization；
For a product to be recommended, the user characteristics square for the multiple users for buying the product to be recommended is obtained
Battle array, and the characteristic value in the user characteristics matrix based on the multiple user, obtain the corresponding production of the product to be recommended
Product eigenmatrix；
The machine learning model for respectively training the user characteristics matrix and product feature Input matrix in advance, it is inclined to obtain user
Good vector sum product preference vector, the user preference vector is used to indicate preference of the target user in product purchase, described
Product preference vector is used to indicate to buy user's feature of the product to be recommended；
According to the user preference vector and product preference vector, obtain between the product to be recommended and the target user
Assessed value is selected, the selection assessed value is for indicating that the target user buys the probability of the product to be recommended；
When the selection assessed value is more than scheduled recommendation threshold value, it is determined that give the Products Show to be recommended to the target
User.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810492957.0A CN108711075A (en) | 2018-05-22 | 2018-05-22 | A kind of Products Show method and apparatus |
| TW108105994A TWI740106B (en) | 2018-05-22 | 2019-02-22 | Product recommendation method and device |
| PCT/CN2019/076240 WO2019223379A1 (en) | 2018-05-22 | 2019-02-27 | Product recommendation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810492957.0A CN108711075A (en) | 2018-05-22 | 2018-05-22 | A kind of Products Show method and apparatus |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108711075A true CN108711075A (en) | 2018-10-26 |
Family
ID=63869364
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810492957.0A Pending CN108711075A (en) | 2018-05-22 | 2018-05-22 | A kind of Products Show method and apparatus |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN108711075A (en) |
| TW (1) | TWI740106B (en) |
| WO (1) | WO2019223379A1 (en) |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109711974A (en) * | 2018-11-20 | 2019-05-03 | 平安科技（深圳）有限公司 | Loan product automatic matching method, device, computer equipment and storage medium |
| CN109859061A (en) * | 2018-12-28 | 2019-06-07 | 阿里巴巴集团控股有限公司 | A kind of recommended method and device of association user |
| CN110020910A (en) * | 2019-01-23 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Object recommendation method and apparatus |
| CN110059248A (en) * | 2019-03-21 | 2019-07-26 | 腾讯科技（深圳）有限公司 | A kind of recommended method, device and server |
| CN110148057A (en) * | 2019-04-30 | 2019-08-20 | 德稻全球创新网络（北京）有限公司 | A kind of structural finance management system of enterprises |
| CN110163723A (en) * | 2019-05-20 | 2019-08-23 | 深圳市和讯华谷信息技术有限公司 | Recommended method, device, computer equipment and storage medium based on product feature |
| CN110162714A (en) * | 2019-01-30 | 2019-08-23 | 腾讯科技（深圳）有限公司 | Content delivery method, calculates equipment and computer readable storage medium at device |
| CN110163662A (en) * | 2019-04-26 | 2019-08-23 | 阿里巴巴集团控股有限公司 | A kind of business model training method, device and equipment |
| CN110223137A (en) * | 2019-05-22 | 2019-09-10 | 平安科技（深圳）有限公司 | Product mix recommended method, device, computer equipment and storage medium |
| CN110472145A (en) * | 2019-07-25 | 2019-11-19 | 维沃移动通信有限公司 | A kind of content recommendation method and electronic equipment |
| WO2019223379A1 (en) * | 2018-05-22 | 2019-11-28 | 阿里巴巴集团控股有限公司 | Product recommendation method and device |
| CN110619585A (en) * | 2019-08-16 | 2019-12-27 | 广州越秀金融科技有限公司 | Method, device, storage medium and processor for recommending data |
| CN110659410A (en) * | 2019-08-16 | 2020-01-07 | 平安科技（深圳）有限公司 | Intelligent recommendation method and device, electronic equipment and computer readable storage medium |
| CN110941758A (en) * | 2019-11-14 | 2020-03-31 | 支付宝(杭州)信息技术有限公司 | Synthetic feature generation method and device of recommendation system |
| CN111078997A (en) * | 2019-11-18 | 2020-04-28 | 中国银行股份有限公司 | Information recommendation method and device |
| CN111461827A (en) * | 2020-03-31 | 2020-07-28 | 中国银行股份有限公司 | Product evaluation information pushing method and device |
| CN111538909A (en) * | 2020-06-22 | 2020-08-14 | 中国银行股份有限公司 | Information recommendation method and device |
| CN111598644A (en) * | 2020-04-01 | 2020-08-28 | 华瑞新智科技（北京）有限公司 | Article recommendation method, device and medium |
| CN111598256A (en) * | 2020-05-18 | 2020-08-28 | 北京互金新融科技有限公司 | Method and device for processing default purchase behavior of target customer |
| CN111626832A (en) * | 2020-06-05 | 2020-09-04 | 中国银行股份有限公司 | Product recommendation method and device and computer equipment |
| CN111652673A (en) * | 2020-05-09 | 2020-09-11 | 腾讯科技（深圳）有限公司 | Intelligent recommendation method, device, server and storage medium |
| WO2020211616A1 (en) * | 2019-04-15 | 2020-10-22 | 北京沃东天骏信息技术有限公司 | Method and device for processing user interaction information |
| CN111814037A (en) * | 2019-12-31 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Data processing method, data processing device, storage medium and electronic equipment |
| CN112101980A (en) * | 2020-08-04 | 2020-12-18 | 北京思特奇信息技术股份有限公司 | Method and system for analyzing purchase preference of user |
| CN112132679A (en) * | 2020-09-30 | 2020-12-25 | 中国银行股份有限公司 | Product recommendation method and device |
| CN112487292A (en) * | 2020-11-27 | 2021-03-12 | 杭州闪援车管家汽车服务有限公司 | Vehicle maintenance system and method based on active recommendation |
| CN112529665A (en) * | 2020-12-18 | 2021-03-19 | 平安银行股份有限公司 | Product recommendation method and device based on combined model and computer equipment |
| WO2021052126A1 (en) * | 2019-09-20 | 2021-03-25 | 平安科技（深圳）有限公司 | Product information recommendation method and apparatus, storage medium, and computer device |
| CN112561588A (en) * | 2020-12-18 | 2021-03-26 | 中国农业银行股份有限公司 | Product recommendation method and device |
| CN112785376A (en) * | 2021-01-20 | 2021-05-11 | 电子科技大学 | Multi-domain recommendation method based on multi-task learning |
| CN113269610A (en) * | 2021-05-25 | 2021-08-17 | 索信达(北京)数据技术有限公司 | Bank product recommendation method and device and storage medium |
| CN113763089A (en) * | 2020-10-27 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Article recommendation method and device and computer-readable storage medium |
| CN115496566A (en) * | 2022-11-16 | 2022-12-20 | 九州好礼(山东)电商科技有限公司 | Regional specialty recommendation method and system based on big data |
| CN115880076A (en) * | 2022-04-20 | 2023-03-31 | 北京中关村科金技术有限公司 | Trusted product recommendation method, device and storage medium |
| CN116611896A (en) * | 2023-07-19 | 2023-08-18 | 山东省人工智能研究院 | Multi-modal recommendation method based on attribute-driven decoupling characterization learning |
| CN116738034A (en) * | 2022-10-10 | 2023-09-12 | 荣耀终端有限公司 | Information pushing method and system |
| CN116738034B (en) * | 2022-10-10 | 2024-06-28 | 荣耀终端有限公司 | Information pushing method and system |
Families Citing this family (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112988921A (en) * | 2019-12-13 | 2021-06-18 | 北京四维图新科技股份有限公司 | Method and device for identifying map information change |
| CN110969491B (en) * | 2019-12-17 | 2023-08-29 | 深圳市梦网视讯有限公司 | Commodity pushing method, system and equipment based on network path |
| CN111210274A (en) * | 2020-01-06 | 2020-05-29 | 北京搜狐新媒体信息技术有限公司 | Advertisement recommendation method and system |
| CN111241408B (en) * | 2020-01-21 | 2023-05-30 | 武汉轻工大学 | Recommendation model construction system and method |
| CN111179041A (en) * | 2020-01-22 | 2020-05-19 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Riding insurance product recommendation method and device |
| CN111429232A (en) * | 2020-04-12 | 2020-07-17 | 中信银行股份有限公司 | Product recommendation method and device, electronic equipment and computer-readable storage medium |
| CN111861635B (en) * | 2020-06-17 | 2022-10-25 | 北京邮电大学 | Friend recommendation method, device and equipment for commodity sharing |
| CN111914188A (en) * | 2020-08-19 | 2020-11-10 | 英华达（上海）科技有限公司 | Method, system, device and storage medium for selecting recommendation target user |
| CN112115358B (en) * | 2020-09-14 | 2024-04-16 | 中国船舶重工集团公司第七0九研究所 | Personalized recommendation method utilizing multi-hop path characteristics in knowledge graph |
| CN112200623A (en) * | 2020-09-27 | 2021-01-08 | 深圳市其乐游戏科技有限公司 | Product recommendation method, device, equipment and storage medium |
| CN113763095B (en) * | 2020-11-27 | 2023-09-26 | 北京京东振世信息技术有限公司 | Information recommendation method and device and model training method and device |
| CN112612955A (en) * | 2020-12-18 | 2021-04-06 | 中国工商银行股份有限公司 | Product pushing method and system based on deep learning |
| CN112632403B (en) * | 2020-12-24 | 2024-04-09 | 北京百度网讯科技有限公司 | Training method, recommendation method, device, equipment and medium for recommendation model |
| CN113222734A (en) * | 2021-05-21 | 2021-08-06 | 中国银行股份有限公司 | Bank financial information recommendation system and method |
| CN113610580B (en) * | 2021-08-10 | 2023-09-19 | 平安科技（深圳）有限公司 | Product recommendation method and device, electronic equipment and readable storage medium |
| CN113781158A (en) * | 2021-08-23 | 2021-12-10 | 湖南大学 | Commodity combination recommendation method and device and storage medium |
| WO2023051678A1 (en) * | 2021-09-29 | 2023-04-06 | 华为技术有限公司 | Recommendation method and related device |
| CN114429384B (en) * | 2021-12-30 | 2022-12-09 | 杭州盟码科技有限公司 | Intelligent product recommendation method and system based on e-commerce platform |
| CN114885185B (en) * | 2022-04-28 | 2024-05-24 | 阿里巴巴（中国）有限公司 | Live broadcast room recommendation method, content recommendation method, terminal and storage medium |
| CN115222461B (en) * | 2022-09-19 | 2023-01-10 | 杭州数立信息技术有限公司 | Intelligent marketing accurate recommendation method |
| CN116823382B (en) * | 2023-05-17 | 2024-01-05 | 南京邮电大学 | Product popularization method based on big data |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110184977A1 (en) * | 2008-09-27 | 2011-07-28 | Jiachun Du | Recommendation method and system based on collaborative filtering |
| CN103246672A (en) * | 2012-02-09 | 2013-08-14 | 中国科学技术大学 | Method and device for performing personalized recommendation on users |
| CN105184618A (en) * | 2015-10-20 | 2015-12-23 | 广州唯品会信息科技有限公司 | Commodity individual recommendation method for new users and system |
| US20150379609A1 (en) * | 2014-06-30 | 2015-12-31 | Kobo Incorporated | Generating recommendations for unfamiliar users by utilizing social side information |
| CN106384259A (en) * | 2016-09-08 | 2017-02-08 | 天津大学 | Recommend system solution method for fusing social information |
| CN106570008A (en) * | 2015-10-09 | 2017-04-19 | 阿里巴巴集团控股有限公司 | Recommendation method and device |
| CN107273438A (en) * | 2017-05-24 | 2017-10-20 | 深圳大学 | A kind of recommendation method, device, equipment and storage medium |
| CN107330115A (en) * | 2017-07-12 | 2017-11-07 | 广东工业大学 | A kind of information recommendation method and device |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7308418B2 (en) * | 2004-05-24 | 2007-12-11 | Affinova, Inc. | Determining design preferences of a group |
| TWI453684B (en) * | 2009-11-24 | 2014-09-21 | Univ Nat Chiao Tung | An Evaluation System and Method of Intelligent Mobile Service Commodity Application Information Retrieval Technology |
| TWI534732B (en) * | 2010-11-15 | 2016-05-21 | Alibaba Group Holding Ltd | Recommended information output method, system and server |
| CN106708883B (en) * | 2015-11-17 | 2020-09-29 | 阿里巴巴集团控股有限公司 | Recommendation method and device |
| CN107330741A (en) * | 2017-07-07 | 2017-11-07 | 北京京东尚科信息技术有限公司 | Graded electron-like certificate uses Forecasting Methodology, device and electronic equipment |
| CN107578270A (en) * | 2017-08-03 | 2018-01-12 | 中国银联股份有限公司 | A kind of construction method, device and the computing device of financial label |
| CN108711075A (en) * | 2018-05-22 | 2018-10-26 | 阿里巴巴集团控股有限公司 | A kind of Products Show method and apparatus |
-
2018
- 2018-05-22 CN CN201810492957.0A patent/CN108711075A/en active Pending
-
2019
- 2019-02-22 TW TW108105994A patent/TWI740106B/en active
- 2019-02-27 WO PCT/CN2019/076240 patent/WO2019223379A1/en active Application Filing
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110184977A1 (en) * | 2008-09-27 | 2011-07-28 | Jiachun Du | Recommendation method and system based on collaborative filtering |
| CN103246672A (en) * | 2012-02-09 | 2013-08-14 | 中国科学技术大学 | Method and device for performing personalized recommendation on users |
| US20150379609A1 (en) * | 2014-06-30 | 2015-12-31 | Kobo Incorporated | Generating recommendations for unfamiliar users by utilizing social side information |
| CN106570008A (en) * | 2015-10-09 | 2017-04-19 | 阿里巴巴集团控股有限公司 | Recommendation method and device |
| CN105184618A (en) * | 2015-10-20 | 2015-12-23 | 广州唯品会信息科技有限公司 | Commodity individual recommendation method for new users and system |
| CN106384259A (en) * | 2016-09-08 | 2017-02-08 | 天津大学 | Recommend system solution method for fusing social information |
| CN107273438A (en) * | 2017-05-24 | 2017-10-20 | 深圳大学 | A kind of recommendation method, device, equipment and storage medium |
| CN107330115A (en) * | 2017-07-12 | 2017-11-07 | 广东工业大学 | A kind of information recommendation method and device |
Cited By (54)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019223379A1 (en) * | 2018-05-22 | 2019-11-28 | 阿里巴巴集团控股有限公司 | Product recommendation method and device |
| CN109711974A (en) * | 2018-11-20 | 2019-05-03 | 平安科技（深圳）有限公司 | Loan product automatic matching method, device, computer equipment and storage medium |
| CN109859061A (en) * | 2018-12-28 | 2019-06-07 | 阿里巴巴集团控股有限公司 | A kind of recommended method and device of association user |
| CN110020910A (en) * | 2019-01-23 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Object recommendation method and apparatus |
| CN110020910B (en) * | 2019-01-23 | 2022-05-24 | 创新先进技术有限公司 | Object recommendation method and device |
| CN110162714A (en) * | 2019-01-30 | 2019-08-23 | 腾讯科技（深圳）有限公司 | Content delivery method, calculates equipment and computer readable storage medium at device |
| CN110162714B (en) * | 2019-01-30 | 2023-11-14 | 腾讯科技（深圳）有限公司 | Content pushing method, device, computing equipment and computer readable storage medium |
| CN110059248A (en) * | 2019-03-21 | 2019-07-26 | 腾讯科技（深圳）有限公司 | A kind of recommended method, device and server |
| CN110059248B (en) * | 2019-03-21 | 2022-12-13 | 腾讯科技（深圳）有限公司 | Recommendation method and device and server |
| WO2020211616A1 (en) * | 2019-04-15 | 2020-10-22 | 北京沃东天骏信息技术有限公司 | Method and device for processing user interaction information |
| CN110163662A (en) * | 2019-04-26 | 2019-08-23 | 阿里巴巴集团控股有限公司 | A kind of business model training method, device and equipment |
| CN110163662B (en) * | 2019-04-26 | 2024-04-05 | 创新先进技术有限公司 | Service model training method, device and equipment |
| CN110148057A (en) * | 2019-04-30 | 2019-08-20 | 德稻全球创新网络（北京）有限公司 | A kind of structural finance management system of enterprises |
| CN110148057B (en) * | 2019-04-30 | 2021-09-14 | 德稻全球创新网络（北京）有限公司 | Enterprise internal structural financing management system |
| CN110163723A (en) * | 2019-05-20 | 2019-08-23 | 深圳市和讯华谷信息技术有限公司 | Recommended method, device, computer equipment and storage medium based on product feature |
| CN110223137A (en) * | 2019-05-22 | 2019-09-10 | 平安科技（深圳）有限公司 | Product mix recommended method, device, computer equipment and storage medium |
| CN110472145A (en) * | 2019-07-25 | 2019-11-19 | 维沃移动通信有限公司 | A kind of content recommendation method and electronic equipment |
| CN110659410A (en) * | 2019-08-16 | 2020-01-07 | 平安科技（深圳）有限公司 | Intelligent recommendation method and device, electronic equipment and computer readable storage medium |
| CN110659410B (en) * | 2019-08-16 | 2024-04-02 | 平安科技（深圳）有限公司 | Intelligent recommendation method and device, electronic equipment and computer readable storage medium |
| CN110619585A (en) * | 2019-08-16 | 2019-12-27 | 广州越秀金融科技有限公司 | Method, device, storage medium and processor for recommending data |
| WO2021052126A1 (en) * | 2019-09-20 | 2021-03-25 | 平安科技（深圳）有限公司 | Product information recommendation method and apparatus, storage medium, and computer device |
| CN110941758B (en) * | 2019-11-14 | 2022-09-16 | 支付宝(杭州)信息技术有限公司 | Synthetic feature generation method and device of recommendation system |
| CN110941758A (en) * | 2019-11-14 | 2020-03-31 | 支付宝(杭州)信息技术有限公司 | Synthetic feature generation method and device of recommendation system |
| CN111078997A (en) * | 2019-11-18 | 2020-04-28 | 中国银行股份有限公司 | Information recommendation method and device |
| CN111078997B (en) * | 2019-11-18 | 2023-08-22 | 中国银行股份有限公司 | Information recommendation method and device |
| CN111814037A (en) * | 2019-12-31 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Data processing method, data processing device, storage medium and electronic equipment |
| CN111814037B (en) * | 2019-12-31 | 2024-04-26 | 北京嘀嘀无限科技发展有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN111461827A (en) * | 2020-03-31 | 2020-07-28 | 中国银行股份有限公司 | Product evaluation information pushing method and device |
| CN111461827B (en) * | 2020-03-31 | 2024-04-09 | 中国银行股份有限公司 | Push method and device for product evaluation information |
| CN111598644B (en) * | 2020-04-01 | 2023-05-02 | 华瑞新智科技（北京）有限公司 | Article recommendation method, device and medium |
| CN111598644A (en) * | 2020-04-01 | 2020-08-28 | 华瑞新智科技（北京）有限公司 | Article recommendation method, device and medium |
| CN111652673B (en) * | 2020-05-09 | 2023-04-07 | 腾讯科技（深圳）有限公司 | Intelligent recommendation method, device, server and storage medium |
| CN111652673A (en) * | 2020-05-09 | 2020-09-11 | 腾讯科技（深圳）有限公司 | Intelligent recommendation method, device, server and storage medium |
| CN111598256A (en) * | 2020-05-18 | 2020-08-28 | 北京互金新融科技有限公司 | Method and device for processing default purchase behavior of target customer |
| CN111598256B (en) * | 2020-05-18 | 2023-08-08 | 北京互金新融科技有限公司 | Processing method and device for default purchase behavior of target client |
| CN111626832A (en) * | 2020-06-05 | 2020-09-04 | 中国银行股份有限公司 | Product recommendation method and device and computer equipment |
| CN111626832B (en) * | 2020-06-05 | 2023-10-03 | 中国银行股份有限公司 | Product recommendation method and device and computer equipment |
| CN111538909A (en) * | 2020-06-22 | 2020-08-14 | 中国银行股份有限公司 | Information recommendation method and device |
| CN112101980B (en) * | 2020-08-04 | 2024-04-02 | 北京思特奇信息技术股份有限公司 | Method and system for analyzing purchasing preference of user |
| CN112101980A (en) * | 2020-08-04 | 2020-12-18 | 北京思特奇信息技术股份有限公司 | Method and system for analyzing purchase preference of user |
| CN112132679A (en) * | 2020-09-30 | 2020-12-25 | 中国银行股份有限公司 | Product recommendation method and device |
| CN113763089A (en) * | 2020-10-27 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Article recommendation method and device and computer-readable storage medium |
| CN112487292A (en) * | 2020-11-27 | 2021-03-12 | 杭州闪援车管家汽车服务有限公司 | Vehicle maintenance system and method based on active recommendation |
| CN112561588A (en) * | 2020-12-18 | 2021-03-26 | 中国农业银行股份有限公司 | Product recommendation method and device |
| CN112529665A (en) * | 2020-12-18 | 2021-03-19 | 平安银行股份有限公司 | Product recommendation method and device based on combined model and computer equipment |
| CN112785376A (en) * | 2021-01-20 | 2021-05-11 | 电子科技大学 | Multi-domain recommendation method based on multi-task learning |
| CN112785376B (en) * | 2021-01-20 | 2022-08-19 | 电子科技大学 | Multi-domain recommendation method based on multi-task learning |
| CN113269610A (en) * | 2021-05-25 | 2021-08-17 | 索信达(北京)数据技术有限公司 | Bank product recommendation method and device and storage medium |
| CN115880076A (en) * | 2022-04-20 | 2023-03-31 | 北京中关村科金技术有限公司 | Trusted product recommendation method, device and storage medium |
| CN116738034A (en) * | 2022-10-10 | 2023-09-12 | 荣耀终端有限公司 | Information pushing method and system |
| CN116738034B (en) * | 2022-10-10 | 2024-06-28 | 荣耀终端有限公司 | Information pushing method and system |
| CN115496566A (en) * | 2022-11-16 | 2022-12-20 | 九州好礼(山东)电商科技有限公司 | Regional specialty recommendation method and system based on big data |
| CN116611896B (en) * | 2023-07-19 | 2023-10-24 | 山东省人工智能研究院 | Multi-modal recommendation method based on attribute-driven decoupling characterization learning |
| CN116611896A (en) * | 2023-07-19 | 2023-08-18 | 山东省人工智能研究院 | Multi-modal recommendation method based on attribute-driven decoupling characterization learning |
Also Published As
| Publication number | Publication date |
|---|---|
| TWI740106B (en) | 2021-09-21 |
| TW202004618A (en) | 2020-01-16 |
| WO2019223379A1 (en) | 2019-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108711075A (en) | A kind of Products Show method and apparatus | |
| CN103823908B (en) | Content recommendation method and server based on user preference | |
| CN108205768A (en) | Database building method and data recommendation method and device, equipment and storage medium | |
| CN108182634A (en) | A kind of training method for borrowing or lending money prediction model, debt-credit Forecasting Methodology and device | |
| CN103678518A (en) | Method and device for adjusting recommendation lists | |
| CN112330358A (en) | Method and apparatus for product sales prediction, storage medium, and electronic device | |
| CN106980999A (en) | The method and apparatus that a kind of user recommends | |
| CN112528164B (en) | User collaborative filtering recall method and device | |
| CN110134783A (en) | Method, apparatus, equipment and the medium of personalized recommendation | |
| CN111611496A (en) | Product recommendation method and device | |
| CN113781139A (en) | Item recommendation method, item recommendation device, equipment and medium | |
| Meilinda et al. | A Platform Based Business Revolution Activates Indonesia's Digital Economy | |
| CN113642923A (en) | Bad asset pack value evaluation method based on historical collection urging data | |
| Zhao et al. | CoEA: A cooperative–competitive evolutionary algorithm for bidirectional recommendations | |
| CN112132634A (en) | Virtual gift resource distribution method and device, computer equipment and storage medium | |
| Chen et al. | Does product recommendation meet its Waterloo in unexplored categories? No, price comes to help | |
| CN106447364A (en) | Information recommending method and information recommending device | |
| Ali et al. | Inferring context with reliable collaborators: a novel similarity estimation method for recommender systems | |
| CN113763089A (en) | Article recommendation method and device and computer-readable storage medium | |
| CN107305662A (en) | Recognize the method and device of violation account | |
| CN110020918A (en) | A kind of recommendation information generation method and system | |
| CN112950304B (en) | Information pushing method, device, equipment and storage medium | |
| Atashpaz Gargari et al. | Bertrand competition for a cellular manufacturing system | |
| CN110427565A (en) | A kind of item recommendation method based on collaborative filtering, intelligent terminal and storage medium | |
| US11386470B2 (en) | Partner fee recommendation service |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181026 |