CN109615487A - Products Show method, apparatus, equipment and storage medium based on user behavior - Google Patents
Products Show method, apparatus, equipment and storage medium based on user behavior Download PDFInfo
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Abstract
The present invention relates to big data analysis fields, a kind of Products Show method, apparatus, equipment and storage medium based on user behavior is provided, this method comprises: obtaining the historical behavior data of user to be recommended, and determines and determine corresponding historical product collection according to the historical behavior data；The true interest scores that the historical product concentrates each historical product are calculated according to the behavior type of the historical behavior data and behavior time of origin, and concentrate the true interest product for determining the user to be recommended in the historical product according to the true interest scores；Acquisition has the recommended products information of the recommended products of incidence relation with the true interest product, and is based on default recommendation rules to the corresponding user terminal push of the user to be recommended recommended products information.The present invention can obtain user's history behavioral data, the interest product of user is analyzed from these historical behavior data, and be associated product push according to the interest product, improve the effect of Products Show.
Description
Technical field
The present invention relates to big data analysis field more particularly to a kind of Products Show method, apparatus based on user behavior,
Equipment and storage medium.
Background technique
With the development of network technology, network becomes one of the main platform that user obtains product.How effectively to net
Network user's recommended products is product supplier's issues that need special attention.
Traditional Products Show method is by largely launching product advertising on internet site, or in certain productions
The homepage recommended location of product platform recommends identical current mainstay product, i.e., recommends identical product to different users.However
This " extensively casting net " formula publicizes behavior, since the indefinite and different user of the product for object and recommendation is to same production
The attention rate of product is different, causes the effect of this recommended method very unsatisfactory；Meanwhile useless product push can also expend user
More energy and time, even can also cause the dislike of user, to affect the popularization of product.
Summary of the invention
It the Products Show method, apparatus that the main purpose of the present invention is to provide a kind of based on user behavior, equipment and deposits
Storage media, it is intended to which realization targetedly carries out Products Show, promotes recommendation effect.
To achieve the above object, the present invention provides a kind of Products Show method based on user behavior, described to be based on user
The Products Show method of behavior includes:
The historical behavior data of user to be recommended are obtained, and determines and determines corresponding go through according to the historical behavior data
History product collection, when the historical behavior data include that the behavior type of historical product and behavior occur for the user to be recommended
Between；
The historical product concentration is calculated according to the behavior type of the historical behavior data and behavior time of origin respectively to go through
The true interest scores of history product, and concentrated according to the true interest scores in the historical product and determine the use to be recommended
The true interest product at family；
The recommended products information for the recommended products that there is incidence relation with the true interest product is obtained, and based on default
Recommendation rules push the recommended products information to the corresponding user terminal of the user to be recommended.
In addition, to achieve the above object, the present invention also provides a kind of Products Show device based on user behavior, the base
Include: in the Products Show device of user behavior
Data acquisition module for obtaining the historical behavior data of user to be recommended, and is determined according to the historical behavior
Data determine corresponding historical product collection, and the historical behavior data include behavior of the user to be recommended to historical product
Type and behavior time of origin；
Interest determination module, for according to the behavior type of the historical behavior data and the calculating of behavior time of origin
Historical product concentrates the true interest scores of each historical product, and is concentrated according to the true interest scores in the historical product
Determine the true interest product of the user to be recommended；
Info push module, the recommendation for obtaining the recommended products for having incidence relation with the true interest product produce
Product information, and default recommendation rules are based on to the corresponding user terminal push of the user to be recommended recommended products information.
In addition, to achieve the above object, the present invention also provides a kind of Products Show equipment based on user behavior, the base
Include processor, memory in the Products Show of user behavior and is stored on the memory and can be by the processor
The Products Show program of execution, wherein when the Products Show program is executed by the processor, realize as it is above-mentioned based on
The step of Products Show method of family behavior.
In addition, to achieve the above object, the present invention also provides a kind of storage medium, being stored with product on the storage medium
Recommended program, wherein realizing when the Products Show program is executed by processor as the above-mentioned product based on user behavior pushes away
The step of recommending method.
The present invention is based on the historical behavior data of user to analyze its true interest, obtains therewith further according to true interest product
Associated recommended products is simultaneously pushed, so that Products Show result meets the actual needs of user, to promote recommendation effect；
Meanwhile when analyzing the true interest of user, carried out based on two dimensions of user behavior type and time of the act, so as to one
Determine non-genuine interest behavior data caused by the factors such as reduction user's automatism browsing, advertisement, marketing activity in degree (to make an uproar
Sound) it is adversely affected to caused by user interest analysis, the interest attenuation situation of user also is simulated in such a way that the time decays,
Further improve the accuracy of interest analysis.
Detailed description of the invention
Fig. 1 is the hardware configuration signal of the Products Show equipment based on user behavior involved in the embodiment of the present invention
Figure；
Fig. 2 is that the present invention is based on the flow diagrams of the Products Show method first embodiment of user behavior；
Fig. 3 is that the present invention is based on the flow diagrams of the Products Show method second embodiment of user behavior；
Fig. 4 is that the present invention is based on the functional block diagrams of the Products Show device first embodiment of user behavior.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present embodiments relate to the Products Show method based on user behavior be mainly used in based on user behavior
Products Show equipment, the Products Show equipment can be the tool such as personal computer (personal computer, PC), server
There is the equipment of data processing function to realize.
Referring to Fig.1, Fig. 1 is the hardware of the Products Show equipment based on user behavior involved in the embodiment of the present invention
Structural schematic diagram.In the embodiment of the present invention, which may include (such as the central processing unit of processor 1001
Central Processing Unit, CPU), communication bus 1002, user interface 1003, network interface 1004, memory
1005.Wherein, communication bus 1002 is for realizing the connection communication between these components；User interface 1003 may include display
Shield (Display), input unit such as keyboard (Keyboard)；Network interface 1004 optionally may include that the wired of standard connects
Mouth, wireless interface (such as Wireless Fidelity WIreless-FIdelity, WI-FI interface)；Memory 1005 can be high speed and deposit at random
Access to memory (random access memory, RAM), is also possible to stable memory (non-volatile memory),
Such as magnetic disk storage, memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.This field
Technical staff is appreciated that hardware configuration shown in Fig. 1 and does not constitute a limitation of the invention, and may include more than illustrating
Or less component, perhaps combine certain components or different component layouts.
With continued reference to Fig. 1, the memory 1005 in Fig. 1 as a kind of computer readable storage medium may include operation system
System, network communication module and Products Show program.In Fig. 1, network communication module is mainly used for connecting database, with data
Library carries out data communication；And processor 1001 can call the Products Show program stored in memory 1005, and execute this hair
The Products Show method based on user behavior that bright embodiment provides.
The Products Show method based on user behavior that the embodiment of the invention provides a kind of.
It is that the present invention is based on the flow diagrams of the Products Show method first embodiment of user behavior referring to Fig. 2, Fig. 2.
In the present embodiment, the Products Show method based on user behavior the following steps are included:
Step S10 obtains the historical behavior data of user to be recommended, and determines and determine institute according to the historical behavior data
Corresponding historical product collection, the historical behavior data include behavior type and behavior of the user to be recommended to historical product
Time of origin；
With the development of network technology, network becomes one of the main platform that user obtains product.How effectively to net
Network user's recommended products is product supplier's issues that need special attention.Traditional Products Show method is by internet net
It stands and above largely launches product advertisings, or recommend identical current mainstay product in the homepage recommended location of certain product platforms,
Recommend identical product to different users.However this " extensively casting net " formula publicizes behavior, due to being directed to object and recommendation
The indefinite and different user of product it is different to the attention rate of identical product, cause the effect of this recommended method to be paid no attention to very much
Think；Meanwhile useless product push can also expend the more energy and time of user, even can also cause that user's is anti-
Sense, to affect the popularization of product.In this regard, the present embodiment proposes a kind of Products Show method based on user behavior, root
According to the true interest of the historical behavior data analysis user of user, the recommendation being associated is obtained further according to true interest product and is produced
Product are simultaneously pushed, so that Products Show result meets the actual needs of user, to promote recommendation effect.
The Products Show method based on user behavior of the present embodiment is real by the Products Show equipment based on user behavior
Existing, which is illustrated by taking recommendation server as an example；And for the product, then it can be the finance such as stock, fund, insurance
Product can also be other digital products, daily necessities etc., be illustrated by taking financial product as an example in the present embodiment.Recommendation server
In order to realize targetedly Products Show, it is necessary first to obtain the historical behavior data of user to be recommended, these historical behavior numbers
According to being user to be recommended in the historical operation behavior to some products, behavior type includes but is not limited to browse, search, click
Product, collection (concern), sharing, purchase etc.；Meanwhile further including the time of origin for having each historical behavior in historical behavior data,
And the object of action product of each historical behavior, and the set of the object of action product of each historical behavior can be described as historical product
Collection；Such as user to be recommended has browsed tri- sections of financial products of A, B, C in x month y day z in 2018, wherein B is the side by search
Formula browsing, and B product is had purchased, C product is paid close attention to, wherein historical product collection includes tri- sections of financial products of A, B, C.
For above-mentioned historical behavior data acquisition, statistical software can be embedded in terminal applies app in advance and develops work
Tool packet SDK (Software Development Kit), is answered when user to be recommended installs the terminal on the user terminal of oneself
When with app, the operation behavior information of user is waited for by the SDK request；When user to be recommended agrees to, user terminal is by root
The behaviors such as search, the browsing of user are recorded according to the inherent statistic logic of SDK, and combine the identity information of user to be recommended
(such as IP address of terminal, user account etc.) generates corresponding historical behavior data, is then sent to the historical behavior data
In recommendation server, at this point, recommendation server can be got by the statistics SDK being mounted in the application of user terminal wait push away
Recommend the historical behavior data of user.Further, it is also possible to be to be configured to bury a logic progress on the script of Related product website
Statistics, when user to be recommended passes through user terminal access product web, if meeting a certain statistical condition, the website of the website
Server will acquire the identity information of the user terminal of current accessed and record its behavior, so that historical behavior data etc. are generated,
And these historical behavior data are sent to recommendation server；And the ASSOCIATE STATISTICS condition for burying a logic, then it can be root
It is configured according to actual conditions, such as can be page stay time and be more than preset time, some element-specific of the page is held
The specific operation of row (such as click, collect), retrieval have used special key words etc..Certainly, in practice, user to be recommended
Historical behavior data are also possible to be collected by the third-party institution, and recommendation server is then to obtain these from the third-party institution
Historical behavior data.
Step S20 calculates the historical product according to the behavior type of the historical behavior data and behavior time of origin
The true interest scores of each historical product are concentrated, and are concentrated described in determination according to the true interest scores in the historical product
The true interest product of user to be recommended；
In the present embodiment, for the historical behavior of user to be recommended, user interest to be recommended can reflect to a certain degree
Place, therefore recommendation server will be carried out when obtaining historical behavior data and historical product collection according to the historical behavior data
Analysis, to determine the interest product of user.But it is worth noting that the historical behavior of user to be recommended is not necessarily to be recommended
User drives the operation of lower progress in the interest (or demand) of its own, it may be possible to be influenced just to promote wait push away by other factors
Recommend the behavior of user's progress；Such as user to be recommended is browsing circle that financial product is entered from other sales promotion windows
Face, this historical behavior are unconscious behaviors；In another example not oneself purchase of some finance of user search to be recommended, but be
It retrieves to friend；It cannot for these historical behaviors and centainly reflect completely where the true interest of user.Meanwhile it is right
In different types of historical behavior, the reflected intensity for treating recommended user's point of interest is also not necessarily identical；Such as use to be recommended
Family has purchased B product, has paid close attention to C product, has browsed A product (not carrying out the especially operation such as other clicks, concern to A product),
In this case, user to be recommended is believed that the interest-degree of tri- kinds of products of A, B, C and is different, and most feels emerging to B product
Secondly interest is respectively C product and A product.In addition, for the time of origin (or data acquisition time) of historical behavior, for
The reflection of user interest also has certain timeliness.Therefore, the present embodiment is in the historical behavior data according to user to be recommended
When analyzing its interest product, two dimensions of behavior type and behavior time of origin of comprehensive historical behavior data are analyzed,
With two dimensions of behavior type and behavior time of origin by the historical behavior data quantization of user to be recommended for corresponding numerical value, and
The numerical value is calculated according to certain algorithm, obtains the true interest for each historical product that user to be recommended was once related to
Scoring, to characterize user to be recommended to the level of interest (or interest possibility) of these historical products, and further, basis should
True scoring determines the true interest product of user to be recommended；Wherein, the true interest scores of some history are higher, then it is believed that
User to be recommended to the historical product it is interested (or user to be recommended to the more interested possibility of the historical product more
It is high).
In the present embodiment, for every historical behavior data, all behavior type including corresponding historical behavior and behaviors
Time of origin further includes the object (financial product being related to) of historical behavior certainly, and the object of these historical behaviors forms one
A historical product collection.And for each historical product that historical product is concentrated, each historical behavior of user to be recommended all can be
Product assigns primary true interest score value addition；It is more for the historical behavior number of a certain product, the true interest of the product
Score value is higher；For example, user to be recommended has browsed A product 2 times, browse B product 5 times, then the true interest score value of B product is wanted
Higher than the true interest score value of A product.Certainly, for different historical behaviors, to the true interest score value addition journey of product
Degree has difference, such as user to be recommended has browsed A product 1 time, has purchased B product 1 time, and buying behavior and browsing behavior phase
Than the level of interest that buying behavior is reflected is higher than the level of interest of browsing behavior, therefore the true interest score value of A product is wanted
Higher than the true interest score value of B product.And for the time of origin of different historical behaviors, then a settable attenuation function, with
Characterizing the decaying of true interest score value addition caused by the historical behavior, (namely characterization user to be recommended is to a certain historical product
Interest attenuation).According to the above description, recommendation server can first count user to be recommended to the behavior type of each historical product
With behavior number, behavior behavior type and behavior number are then quantified as corresponding numerical value, and according to behavior time of origin
Logarithm carries out attenuation processing, to obtain the true interest scores of each historical product.
Specifically, calculating the historical product collection according to the behavior type of the historical behavior data and behavior time of origin
In each historical product true interest scores the step of include:
One subseries is carried out to the historical behavior data according to historical product corresponding to the historical behavior data, is obtained
To the corresponding product class behavior data of each historical product；
For the historical behavior data that step S10 is obtained, recommendation server can be first with its object of action (namely behavior pair
The historical product answered) classify, to obtain the corresponding various product class behavior data of each historical product.For example, A product
Product class behavior data include, and when x1 month y1 day z1 in 2017 has purchased A production when having browsed A product, x1 month y2 day z2 in 2017
Product；The product class behavior data of B product have browsed B product when being x1 month y1 day z1 in 2017, and when x1 month y3 day z3 in 2017 is clear
B product is look at.By above-mentioned processing, the behavioral data that user to be recommended is directed to every kind of historical product can be obtained.
The behavior number of each behavior type in the product class behavior data is counted respectively, and determines each behavior type most
Nearly time of the act；
When obtaining product class behavior data, recommendation server will be for statistical analysis to each product class data, and determine
The behavior number of each behavior type in each product class behavior data, and determine that the nearest time of the act of each behavior type is (last
The primary time that the behavior occurs).For example, for the product class behavior data of A product, including the purchase of M browsing behavior, n times
Behavior, wherein being purchased to when the nearest time of the act of A product progress browsing behavior is x1 month y1 day z1 in 2017 to A product
When the nearest time of the act for buying behavior is x1 month y2 day z2 in 2017.By above-mentioned processing, user to be recommended can be obtained for every
The behavioural characteristic (the nearest time of the act of behavior number and each behavior type including each behavior type) of kind historical product.
Respectively by the nearest behavior of the behavior number and each behavior type of the corresponding each behavior type of each historical product
Time is substituting to default interest and divides in formula, to calculate the true interest score value of each historical product.
User to be recommended is being obtained to the nearest of the behavior number of the behavior type of every kind of historical product and each behavior type
When time of the act, behavior number and nearest time of the act can be substituting to a default interest and divided in formula, respectively gone through to calculate
The true interest score value of history product.As above-mentioned, preset interest at this and divide formula, different behavior types has different interest
Score value addition；The behavior number of same behavior type is more, and the interest score value addition of behavior type is higher；Meanwhile every kind of row
It will be decayed by the time and influenced for the interest score value of type.In this regard, the default interest divides formula can be with are as follows:
Wherein, P is the true interest score value of each historical product；
N is the type for the behavior type for including, n >=1 in product class behavior data；
QiFor the corresponding default score value addition of i-th kind of historical behavior in product class behavior data, Qi>=0, i >=1；
KiFor the behavior number of i-th kind of historical behavior in product class behavior data, Ki≥0；
F(ti-t0) it is time attenuation function, tiFor the nearest behavior of i-th kind of historical behavior in the product class behavior data
Time, t0For current time；F(ti-t0) and ti-t0Negative correlation, ti-t0It is smaller, F (ti-t0) bigger；Such as F (ti-t0)=mlog
(ti-t0)；In another exampleWherein e is natural logrithm, and λ is the constant greater than 0.
It is worth noting that dividing formula also for above-mentioned default interest can be adjusted according to the actual situation and convert.
In the present embodiment, when historical product being calculated concentrating the corresponding true interest scores of each historical product
Several highest historical products of true interest scores are determined as the true interest product of user to be recommended (certainly for true
The quantity of interest product can self-defining according to the actual situation)；It or is that true interest score value is higher than a default interest
The historical product of threshold value is as true interest product (when the history for being higher than a default interest threshold there is no true interest score value produces
When product, can at random using a historical product as true interest product, or using the true highest historical product of interest score value as
True interest product).In addition, if a variety of different types of historical products involved in historical product collection, can also determine such respectively
The true interest product of type, such as the financial history product of user to be recommended includes stock and fund product, then can determine respectively
The true interest product of stock class and the true interest product of fund class.
Step S30 obtains the recommended products information for the recommended products for having incidence relation with the true interest product, and
The recommended products information is pushed to the corresponding user terminal of the user to be recommended based on default recommendation rules.
It, can be true emerging according to this when recommendation server determines the true interest product of user to be recommended in the present embodiment
Interesting product carries out targetedly product and inquires, and obtains the recommended products for having incidence relation with the true interest product, and obtain
The recommended products information of the recommended products.Wherein, it for the incidence relation, can be in different types of true interest product
There is different embodiments.For example, the incidence relation can, fortune similar with amount of money range for financial products such as stock, fund, insurances
Seek mechanism is identical, risk class is similar etc.；For digital product, which can be that function is identical, price is similar, brand
It is mutually same.In the present embodiment, recommendation server, can be pre- according to one in the recommended products information for getting recommended products product
If recommendation rules push the recommended products information to the user terminal of recommended user.Wherein, which may include
Recommend the regulation of the contents such as time, push frequency, propelling data amount.
Optionally, when the product type of true interest product is fund, the acquisition has with the true interest product
The recommended products information Step of relevant recommended products includes:
Determine the fund risk type of the true interest product and the heavy storehouse burst ticket that the true interest product is held, and
Determine the industry type of the heavy storehouse burst ticket；
In the present embodiment, when the product type of true interest product is fund, recommendation server can first determine that this is true
The fund risk type of interest product (fund), such as conservative, steady type, type of keeping forging ahead, wherein different fund risk types
Corresponding different risk class.The stock that recommendation server also will acquire the true interest product (fund) simultaneously holds information,
It includes that stock name, the affiliated industry of each issuance of shares person, each stock hold market value etc. that the stock, which holds information,；It then can be according to this
Stock holds the heavy storehouse burst ticket that information determines the true interest product, and wherein the heavy storehouse burst ticket is the highest stock of carrying market.
When determining heavy storehouse burst ticket, recommendation server will also determine industry type (the i.e. affiliated row of issuance of shares person of the heavy storehouse burst ticket
Industry).
Inquire the corresponding optional stock of the industry type, and according to the optional stock predetermined period the change of stock price
Determine the Stock Risk type of the optional stock；
In the present embodiment, when determining the industry type of heavy storehouse burst ticket, recommendation server can by inquiry the sector type
It selects stocks ticket, and obtains the change of stock price information of these optional stocks in predetermined period, then according to these the change of stock price information
Determine the Stock Risk type of these optional stock types.For example, with " one week " for the period, if the share price extreme value wave of a certain stock
Dynamic range is less than 5%, then the Stock Risk type of the stock is conservative；If the share price extreme value fluctuation range of the stock exists
Between 5% to 10%, then the Stock Risk type of the stock is steady type；If the share price extreme value fluctuation range of a certain stock is big
In 10%, then the Stock Risk type of the stock is type etc. of keeping forging ahead.
It is determined and the true interest in the optional stock according to the Stock Risk type and fund risk type
The associated recommendation stock of product, and the recommendation stock is determined as recommended products；
It, can be according to Stock Risk type and fund when determining the Stock Risk type of each optional stock in the present embodiment
The determining optional stock with the true interest product with identical (or similar) risk class of risk classifications, such as be all conservative
Type is all steady type etc.；The optional stock be with the true associated recommendation stock of interest product, and by the recommendation stock
It is determined as recommended products.
Obtain the recommended products information of the recommended products.
In the present embodiment, when determining recommended products, recommendation server can obtain the recommended products letter of the recommended products
Breath, to be pushed to user to be recommended.
Above by heavy storehouse burst ticket determining from the interest fund of user to be recommended, then the affiliated industry from the heavy storehouse burst ticket
Stock is recommended in middle selection, to realize, recommended products is similar to the industry field of interest product, and also helping reduces interest base
The daily operation operation of the operating agency of gold is adversely affected caused by recommending stock；And will also when stock is recommended in selection
Consider that the risk of user bears grade, improves the interest compactness of recommended products and user.
Optionally, when the product type of true interest product is stock, the acquisition has with the true interest product
The recommended products information Step of relevant recommended products includes:
It determines that maximum amount holds the maximum amount fund of the true interest product, and determines the operation of the maximum amount fund
Mechanism；
In the present embodiment, when the product type of true interest product is stock, recommendation server can first determine the stock
Holder, wherein holding for the stock, may include fund mechanism, individual, company etc.；Then recommendation server can be at these
It determines that maximum amount holds the maximum amount fund of the stock in holder, namely possesses the most funds of share of the stock, and determine
The operating agency of the maximum amount fund.
The optional fund of the operating agency operation is inquired, and the optional fund is determined as recommended products；
In the operating agency of maximum amount fund for determining the stock, recommendation server will be inquired the operating agency and be runed
All optional (commercially available) funds, and using these optional funds as recommended products.
Obtain the recommended products information of the recommended products.
In the present embodiment, when determining recommended products, recommendation server can obtain the recommended products letter of the recommended products
Breath, to be pushed to user to be recommended.
The fund product for holding the fund operating agency of interest stock above by recommendation great number, is allowed with the angle of operator
User understands other fund products with the stock, and user is facilitated to get its requirement product, promotes recommendation effect.
In the present embodiment, in order to further increase the effect of Products Show, can also according to historical behavior data analyze to
The browsing of recommended user is accustomed to, and then carries out targetedly Products Show according to the browsing of user habit.Specifically, recommendation service
The historical behavior data that device can treat recommended user are analyzed, thus when obtaining the high frequency browsing of the user to be recommended
Section；Such as user to be recommended had 5 days 12 points to 12 points 20 30 minutes, at night 22: to 22 points browsings at noon within past 7 days
Product, then it is believed that target user high frequency browsing the period be 12 noon to 12 points 30 minutes, at night 22 points to 22 points 20 minutes.When
So, is browsed for different high frequencies the period, corresponding period duration may be different, the information content that user to be recommended can check
Difference, therefore, it is recommended that server is also by the period duration of determination each high frequency browsing period.
When detecting that current time is in the high frequency browsing period, recommendation server will be browsed according to the high frequency being presently in
The period duration of period determines recommendation information amount；Wherein for the relationship of the recommendation information amount and period duration, can be preset
One rule is configured, such as 10 minutes corresponding 1 products of duration, and corresponding 3 products of 20 minutes durations etc., this is pushed away certainly
It recommends information content to be also possible to be characterized according to the type of product information, such as 10 minutes duration corresponding product titles and letter
It is situated between, 20 minutes durations correspond to being discussed in detail for the product.When determining recommendation information amount, recommendation server can be according to this
Recommendation information amount pushes corresponding recommended products information to the user terminal of user to be recommended.In the above manner, may make production
The time and information content that product are recommended can be accustomed to closer to the browsing of user to be recommended, and reducing invalid push causes user to dislike
Situation is conducive to promote recommendation effect.
In the present embodiment, by obtaining the historical behavior data of user to be recommended, and determine according to the historical behavior number
According to corresponding historical product collection is determined, the historical behavior data include behavior class of the user to be recommended to historical product
Type and behavior time of origin；The historical product is calculated according to the behavior type of the historical behavior data and behavior time of origin
The true interest scores of each historical product are concentrated, and are concentrated described in determination according to the true interest scores in the historical product
The true interest product of user to be recommended；The recommendation for obtaining the recommended products for having incidence relation with the true interest product produces
Product information, and default recommendation rules are based on to the corresponding user terminal push of the user to be recommended recommended products information.
By the above form, the present embodiment analyzes its true interest based on the historical behavior data of user, further according to true interest product
It obtains the recommended products being associated and is pushed, so that Products Show result meets the actual needs of user, to be promoted
Recommendation effect；Meanwhile when analyzing the true interest of user, carried out based on two dimensions of user behavior type and time of the act,
So as to reduce non-genuine interest caused by the factors such as user's automatism browsing, advertisement, marketing activity to a certain extent
Behavioral data (noise) adversely affects caused by analyzing user interest, also simulates the emerging of user in such a way that the time decays
Interesting attenuation further improves the accuracy of interest analysis.
It is that the present invention is based on the flow diagrams of the Products Show method second embodiment of user behavior referring to Fig. 3, Fig. 3.
Based on above-mentioned embodiment illustrated in fig. 2, in the present embodiment, the recommended products information includes manual service link, step
After rapid S30 further include:
Step S40, when receiving the manual service request that the user terminal is received and sent based on the manual service chain,
Corresponding artificial customer side is inquired according to the recommended products information, and sends corresponding service role to the artificial customer side
Information.
In the present embodiment, it is contemplated that user to be recommended might have query after the recommended products information for having browsed push,
User to be recommended seeks advice from for convenience, can also provide attendant consultation service service in the present embodiment for user to be recommended.Specifically
, it include manual service link in the recommended products information that recommendation server is pushed；User to be recommended is passing through user's end
After end has browsed the recommended products information of push, attendant consultation service if desired is carried out to contact staff, then can pass through user terminal point
Hit the manual service link, thus triggering for manual service request；User terminal should according to the operation of user to be recommended
Manual service request is sent to recommendation server.Recommendation server, first will be according to this when receiving manual service request
The corresponding artificial customer side of the recommended products information inquiry (end of the business personnel that is responsible for the credit product, product manager et al.
End), and corresponding service role information is sent to the artificial customer side；Wherein the service role information may include user's end
IP address, name on account, the telephone number at end etc., so that contact staff can pass through artificial customer side and user to be recommended
It is contacted, provides manual service for target user, promote the service experience of target user.
In addition, the embodiment of the present invention also provides a kind of Products Show device based on user behavior.
It is that the present invention is based on the signals of the functional module of the Products Show device first embodiment of user behavior referring to Fig. 4, Fig. 4
Figure.
In the present embodiment, the Products Show device based on user behavior includes:
Data acquisition module 10 for obtaining the historical behavior data of user to be recommended, and is determined according to the history row
Corresponding historical product collection is determined for data, the historical behavior data include row of the user to be recommended to historical product
For type and behavior time of origin；
Interest determination module 20, for calculating institute according to the behavior type and behavior time of origin of the historical behavior data
The true interest scores that historical product concentrates each historical product are stated, and according to the true interest scores in the historical product collection
The true interest product of the middle determination user to be recommended；
Info push module 30, for obtaining the recommendation for the recommended products that there is incidence relation with the true interest product
Product information, and believed based on default recommendation rules to the corresponding user terminal push of the user to be recommended recommended products
Breath.
Wherein, each virtual functions module of the above-mentioned Products Show device based on user behavior, which is stored in shown in Fig. 1, is based on
It is functional for realizing the institute of Products Show program in the memory 1005 of the Products Show equipment of user behavior；Each module quilt
, it can be achieved that obtaining user's history behavioral data when processor 1001 executes, and analyze user's from these historical behavior data
Interest product, and the function that product pushes is associated according to the interest product.
Further, the interest determination module 20 includes:
Data sorting unit, for the historical product according to corresponding to the historical behavior data to the historical behavior number
According to classifying, the corresponding product class behavior data of each historical product are obtained；
Data statistics unit, for counting the behavior number of each behavior type in the product class behavior data respectively, and
Determine the nearest time of the act of each behavior type；
Score value computing unit, for respectively by the behavior number and each row of the corresponding each behavior type of each historical product
It is substituting to default interest for the nearest time of the act of type and divides in formula, to calculate the true interest point of each historical product
Value.
Further, the default interest divides formula are as follows:
Wherein, P is the true interest score value of each historical product；
N is the type for the behavior type for including, n >=1 in the product class behavior data；
QiFor the corresponding default score value addition of i-th kind of historical behavior in the product class behavior data, Qi>=0, i >=1；
KiFor the behavior number of i-th kind of historical behavior in the product class behavior data, Ki≥0；
F(ti-t0) it is time attenuation function, tiFor the nearest behavior of i-th kind of historical behavior in the product class behavior data
Time, t0For current time；F(ti-t0) and ti-t0It is negatively correlated.
Further, the product type of the true interest product is fund, and the interest determination module 20 includes:
First determination unit determines that the fund risk type of the true interest product and the true interest product are held
Heavy storehouse burst ticket, and determine the industry type of the heavy storehouse burst ticket；
Second determination unit exists for inquiring the corresponding optional stock of the industry type, and according to the optional stock
The change of stock price of predetermined period determines the Stock Risk type of the optional stock；
Third determination unit, for true in the optional stock according to the Stock Risk type and fund risk type
The fixed and true associated recommendation stock of interest product, and the recommendation stock is determined as recommended products；
First acquisition unit, for obtaining the recommended products information of the recommended products.
Further, the product type of the true interest product is stock, and the interest determination module 20 includes:
4th determination unit for determining that maximum amount holds the maximum amount fund of the true interest product, and determines institute
State the operating agency of maximum amount fund；
5th determination unit is determined for inquiring the optional fund of the operating agency operation, and by the optional fund
For recommended products；
Second acquisition unit, for obtaining the recommended products information of the recommended products.
Further, the info push module 30 includes:
Period acquiring unit, when the high frequency for the user to be recommended according to the historical behavior data acquisition browses
Section, and determine the period duration of the high frequency browsing period；
Information push unit is used for when current time is in the high frequency browsing period, clear according to high frequency is presently in
The period duration of period of looking at determines recommendation information amount, and according to the recommendation information amount to the user terminal of the user to be recommended
Push the recommended products information.
Further, the recommended products information includes manual service link, the Products Show based on user behavior
Further include:
Task sending module, in the artificial clothes for receiving the user terminal and being received and sent based on the manual service chain
When business request, corresponding artificial customer side is inquired according to the recommended products information, and send and correspond to the artificial customer side
Service role information.
Wherein, the function of modules is realized with above-mentioned based on user in the above-mentioned Products Show device based on user behavior
Each step is corresponding in the Products Show embodiment of the method for behavior, and function and realization process no longer repeat one by one here.
In addition, the embodiment of the present invention also provides a kind of storage medium.
Products Show program is stored on storage medium of the present invention, wherein the Products Show program is executed by processor
When, it realizes such as the step of the above-mentioned Products Show method based on user behavior.
Wherein, Products Show program, which is performed realized method, can refer to that the present invention is based on the products of user behavior to push away
Each embodiment of method is recommended, details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the system that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (it can be mobile phone,
Computer, server, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of Products Show method based on user behavior, which is characterized in that the Products Show side based on user behavior
Method includes:
The historical behavior data of user to be recommended are obtained, and determine corresponding historical product collection according to the historical behavior data,
The historical behavior data include behavior type and behavior time of origin of the user to be recommended to historical product；
Calculating the historical product according to the behavior type of the historical behavior data and behavior time of origin concentrates each history to produce
The true interest scores of product, and concentrated according to the true interest scores in the historical product and determine the user's to be recommended
True interest product；
The recommended products information for the recommended products that there is incidence relation with the true interest product is obtained, and is recommended based on default
Rule pushes the recommended products information to the corresponding user terminal of the user to be recommended.
2. the Products Show method based on user behavior as described in claim 1, which is characterized in that described according to the history
The behavior type and behavior time of origin of behavioral data calculate the true interest scores that the historical product concentrates each historical product
The step of include:
Classified according to historical product corresponding to the historical behavior data to the historical behavior data, obtains each history
The corresponding product class behavior data of product；
The behavior number of each behavior type in the product class behavior data is counted respectively, and determines the nearest row of each behavior type
For the time；
Respectively by the nearest time of the act of the behavior number of the corresponding each behavior type of each historical product and each behavior type
It is substituting to default interest to divide in formula, to calculate the true interest score value of each historical product.
3. the Products Show method based on user behavior as claimed in claim 2, which is characterized in that the default interest point public affairs
Formula are as follows:
Wherein, P is the true interest score value of each historical product；
N is the type for the behavior type for including, n >=1 in the product class behavior data；
QiFor the corresponding default score value addition of i-th kind of historical behavior in the product class behavior data, Qi>=0, i >=1；
KiFor the behavior number of i-th kind of historical behavior in the product class behavior data, Ki≥0；
F(ti-t0) it is time attenuation function, tiFor in the product class behavior data when the nearest behavior of i-th kind of historical behavior
Between, t0For current time；F(ti-t0) and ti-t0It is negatively correlated.
4. the Products Show method based on user behavior as described in claim 1, which is characterized in that the true interest product
Product type be fund,
The recommended products information Step of recommended products of the acquisition with the true interest product with incidence relation include:
It determines the fund risk type of the true interest product and the heavy storehouse burst ticket that the true interest product is held, and determines
The industry type of the heavy storehouse burst ticket；
The corresponding optional stock of the industry type is inquired, and the change of stock price according to the optional stock in predetermined period determines
The Stock Risk type of the optional stock；
It is determined and the true interest product in the optional stock according to the Stock Risk type and fund risk type
Associated recommendation stock, and the recommendation stock is determined as recommended products；
Obtain the recommended products information of the recommended products.
5. the Products Show method based on user behavior as described in claim 1, which is characterized in that the true interest product
Product type be stock,
The recommended products information Step of recommended products of the acquisition with the true interest product with incidence relation include:
It determines that maximum amount holds the maximum amount fund of the true interest product, and determines the operation machine of the maximum amount fund
Structure；
The optional fund of the operating agency operation is inquired, and the optional fund is determined as recommended products；
Obtain the recommended products information of the recommended products.
6. the Products Show method based on user behavior as described in claim 1, which is characterized in that described to be recommended based on default
The step of rule pushes the recommended products information to the corresponding user terminal of the user to be recommended include:
The high frequency of the user to be recommended according to the historical behavior data acquisition browses the period, and when determining high frequency browsing
The period duration of section；
When current time is in the high frequency browsing period, pushed away according to the period duration determination for being presently in the high frequency browsing period
Information content is recommended, and the recommended products information is pushed to the user terminal of the user to be recommended according to the recommendation information amount.
7. such as the Products Show method described in any one of claims 1 to 6 based on user behavior, which is characterized in that described
Recommended products information includes manual service link,
The acquisition has the recommended products information of the recommended products of incidence relation with the true interest product, and based on default
After the step of recommendation rules push the recommended products information to the corresponding user terminal of the user to be recommended, further includes:
When receiving the manual service request that the user terminal is received and sent based on the manual service chain, according to the recommendation
The corresponding artificial customer side of information query, and corresponding service role information is sent to the artificial customer side.
8. a kind of Products Show device based on user behavior, which is characterized in that the Products Show dress based on user behavior
It sets and includes:
Data acquisition module for obtaining the historical behavior data of user to be recommended, and is determined according to the historical behavior data
Determine corresponding historical product collection, the historical behavior data include behavior type of the user to be recommended to historical product
With behavior time of origin；
Interest determination module, for calculating the history according to the behavior type and behavior time of origin of the historical behavior data
Product concentrates the true interest scores of each historical product, and is concentrated and determined in the historical product according to the true interest scores
The true interest product of the user to be recommended；
Info push module, the recommended products for obtaining the recommended products for having incidence relation with the true interest product are believed
Breath, and default recommendation rules are based on to the corresponding user terminal push of the user to be recommended recommended products information.
9. a kind of Products Show equipment based on user behavior, which is characterized in that the Products Show packet based on user behavior
It includes processor, memory and is stored in the Products Show program that can be executed on the memory and by the processor, wherein
When the Products Show program is executed by the processor, realize as described in any one of claims 1 to 7 based on user's row
For Products Show method the step of.
10. a kind of storage medium, which is characterized in that Products Show program is stored on the storage medium, wherein the product
When recommended program is executed by processor, the Products Show based on user behavior as described in any one of claims 1 to 7 is realized
The step of method.
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| PCT/CN2019/092100 WO2020140399A1 (en) | 2019-01-04 | 2019-06-20 | Method, apparatus, and device for product recommendation based on user behavior, and storage medium |
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| CN201910014579.XA CN109615487A (en) | 2019-01-04 | 2019-01-04 | Products Show method, apparatus, equipment and storage medium based on user behavior |
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| CN112650921A (en) * | 2020-12-18 | 2021-04-13 | 中国平安人寿保险股份有限公司 | Object recommendation method, device, equipment and storage medium |
| CN112650921B (en) * | 2020-12-18 | 2024-06-18 | 中国平安人寿保险股份有限公司 | Object recommendation method, device, equipment and storage medium |
| CN112802454B (en) * | 2020-12-31 | 2023-02-21 | 大众问问(北京)信息科技有限公司 | Method and device for recommending awakening words, terminal equipment and storage medium |
| CN112802454A (en) * | 2020-12-31 | 2021-05-14 | 大众问问(北京)信息科技有限公司 | Method and device for recommending awakening words, terminal equipment and storage medium |
| CN113486254A (en) * | 2021-08-02 | 2021-10-08 | 南京邮电大学 | Activity recommendation method and system based on big data |
| CN113343024A (en) * | 2021-08-04 | 2021-09-03 | 北京达佳互联信息技术有限公司 | Object recommendation method and device, electronic equipment and storage medium |
| CN113781246B (en) * | 2021-09-14 | 2024-03-05 | 平安科技（深圳）有限公司 | Strategy generation method and device based on preset label and storage medium |
| CN113781246A (en) * | 2021-09-14 | 2021-12-10 | 平安科技（深圳）有限公司 | Policy generation method and device based on preset label and storage medium |
| CN114119168A (en) * | 2021-12-01 | 2022-03-01 | 中国建设银行股份有限公司 | Information pushing method and device |
| CN114782199A (en) * | 2022-05-18 | 2022-07-22 | 广东博众智能科技投资有限公司 | Data display method, device, equipment and medium |
| CN115482115A (en) * | 2022-09-22 | 2022-12-16 | 广州飞卓科汇信息科技有限公司 | Fund information interaction method and system based on big data |
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