CN115983902B - Information pushing method and system based on user real-time event - Google Patents
Information pushing method and system based on user real-time event Download PDFInfo
- Publication number
- CN115983902B CN115983902B CN202310036987.1A CN202310036987A CN115983902B CN 115983902 B CN115983902 B CN 115983902B CN 202310036987 A CN202310036987 A CN 202310036987A CN 115983902 B CN115983902 B CN 115983902B
- Authority
- CN
- China
- Prior art keywords
- event
- user
- attribute
- behavior type
- real
- 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.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The present disclosure provides an information pushing method and system based on a user real-time event, comprising constructing an event model corresponding to the real-time event data based on the acquired real-time event data, analyzing the event model, and determining an event attribute and a user behavior type corresponding to the real-time event data; carrying out unique matching on the event attribute and the user behavior type and preset rules in an event library, if so, determining preference information of the user according to the user behavior type and the user history information, and determining state information of the user according to the event attribute and the user history information; and determining a scene where the user is based on the preference information and the state information by combining the real-time event data, and pushing information according to trigger tag conditions corresponding to the scene where the user is. The method disclosed by the invention achieves accurate cognition and matching of the client demands and improves the marketing success rate.
Description
Technical Field
The disclosure relates to information pushing technology, in particular to an information pushing method and system based on a user real-time event.
Background
At present, enterprises generally face a plurality of marketing pain problems such as serious product homogenization, more serious competition and the like, and a traditional marketing strategy is to set popularization activities or product recommendations according to analysis of client historical data and mining client characteristics. However, the demands of customers at the present stage are random, various and time-efficient, the traditional marketing strategies are no longer suitable for the business development requirements at the present stage, and the development of real-time event marketing around the demands of customers has become a future development trend.
The existing traditional marketing mode has the following defects:
1. at present, the demands of customers are more time-efficient, the window period for capturing marketing opportunities is too short, and proper marketing activities cannot be pushed to proper customers in real time;
2. customer behavior and events lack collaboration between channels, and customer events of different channels cannot be correlated.
3. The guest groups and activities configured by the traditional marketing mode are difficult to multiplex, so that the past marketing experience cannot be precipitated into a guest group model and strategy system.
4. The marketing process is complex in configuration, low in automation degree and long in development period, and when a plurality of marketing activities have relevance, a large amount of manual configuration is required, so that the management cost is high, and the marketing efficiency is low.
The information disclosed in the background section of the application is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides an information pushing method and system based on a user real-time event, which can at least solve part of problems in the prior art, realize real-time mastering and analyzing of a marketing event and pointedly push a marketing activity for the event, thereby achieving accurate cognition and matching of customer demands and improving the marketing success rate.
In a first aspect of embodiments of the present disclosure,
the information pushing method based on the user real-time event comprises the following steps:
based on the acquired real-time event data, constructing an event model corresponding to the real-time event data, analyzing the event model, and determining event attributes and user behavior types corresponding to the real-time event data;
the event attribute and the user behavior type are subjected to unique matching with preset rules in an event library, if the event attribute and the user behavior type are matched with the preset rules, preference information of the user is determined according to the user behavior type and the user history information, and state information of the user is determined according to the event attribute and the user history information, wherein the event library is used for receiving the real-time event data and carrying out unique matching on the real-time event data and the preset rules;
Based on the preference information and the state information, combining the real-time event data, determining a scene where a user is located, and pushing information according to trigger tag conditions corresponding to the scene where the user is located, wherein the trigger tag conditions are used for pushing corresponding tag contents under the condition that trigger rules are indicated to be met.
In an alternative embodiment of the present invention,
the event attributes comprise a first attribute and a second attribute, wherein the first attribute is used for indicating the attribute that the association with the user tag is larger than an association threshold value, and the second attribute is used for indicating the attribute that the association with the user tag is smaller than the association threshold value; the user behavior types comprise a first behavior type and a second behavior type, wherein the first behavior type is used for indicating the behavior type matched with the user tag, and the second behavior type is used for indicating the accidental behavior type;
the uniquely matching the event attribute, the user behavior type and the preset rule in the event library comprises the following steps:
respectively distributing corresponding weight values for the event attribute and the user behavior type, wherein if the event attribute is a first attribute, a first weight value is distributed for the event attribute, and if the event attribute is a second attribute, a second weight value is distributed for the event attribute, and the first weight value is larger than the second weight value;
If the user behavior type is the first behavior type, a third weight value is allocated to the user behavior type, and if the user behavior type is the second behavior type, a fourth weight value is allocated to the user behavior type, wherein the third weight value is larger than the fourth weight value;
determining a rule characteristic value corresponding to the real-time event data according to the weight value, the event attribute and the user behavior type;
and calculating a plurality of space distances from the preset rules in the event library based on the rule characteristic values, and taking the preset rule with the smallest distance in the plurality of space distances as the rule uniquely matched with the real-time event data.
In an alternative embodiment of the present invention,
the method for allocating the corresponding weight value to the event attribute and the user behavior type comprises the following steps:
respectively distributing corresponding evaluation indexes for the event attribute and the user behavior type, and constructing a corresponding evaluation index matrix;
based on the evaluation index matrixes, respectively giving a corresponding evaluation weight vector to each evaluation index matrix through subjective and objective mixing strategy weighting;
and distributing corresponding evaluation indexes for the event attribute and the user behavior type through the evaluation weight vector, determining a weight balance point based on a preset target gain function, and distributing corresponding weight values for the event attribute and the user behavior type according to the determined weight balance point and the evaluation weight vector.
In an alternative embodiment of the present invention,
the assigning of the corresponding evaluation weight vector to each evaluation index matrix through subjective and objective mixing strategy weighting based on the evaluation index matrix comprises the following steps:
wherein W represents an evaluation weight vector, M, N represents the number of evaluation indexes and the number of evaluation objects respectively, wherein the evaluation objects comprise the event attribute and the user behavior type, a and b represent a first weight duty ratio and a second weight duty ratio respectively, W1 and W2 represent a first strategy weighting matrix and a second strategy weighting matrix respectively, xij represents an evaluation index matrix, and represents a contribution value of an ith evaluation index at a jth evaluation object.
In an alternative embodiment of the present invention,
the determining the scene of the user based on the preference information and the state information in combination with the real-time event data, and pushing the information according to the trigger tag condition corresponding to the scene of the user comprises the following steps:
extracting preference features from the preference information and extracting state features from the state information respectively, and taking the preference features and the state features as user features;
extracting a plurality of event features from the real-time event data, and calculating the similarity of any two event features in the plurality of event features, wherein the event features with the similarity larger than a preset similarity threshold value are used as an initial event feature set;
And determining the association degree of the association event feature set and the scene where the user is located, judging whether the event feature with the highest association degree accords with the trigger tag condition, and pushing information if so, wherein the association event feature set is used for indicating event features of an intersection set generated between the initial event feature set and the scene where the user is located.
In an alternative embodiment of the present invention,
the triggering label conditions comprise a first triggering condition and a second triggering condition, wherein the first triggering condition is used for indicating that the preference information and the label corresponding to the state information are triggered in real time when being matched with the prestored label in the event library, the second triggering condition is used for indicating that the preference information and the label corresponding to the state information are triggered in delayed mode when being not matched with the prestored label in the event library,
the step of pushing information according to the trigger tag conditions corresponding to the scene where the user is located comprises the following steps:
if the triggering tag condition is a first triggering condition, information pushing is performed in real time;
and if the trigger tag condition is a second trigger condition, carrying out information push in a delayed manner.
In a second aspect of the embodiments of the present disclosure,
Providing a user real-time event based information pushing system applying the user real-time event based information pushing method of any one of the previous claims, the system comprising:
the data source module is used for collecting original real-time event data through the collector and pushing the data to the first rubbidq queue to provide basic data sources for the event library module;
the event library module is used for processing the real-time event data through the link according to the basic attribute and the expansion attribute, checking whether the attribute is consistent with a preset rule, and pushing the real-time event data to a second rabkitmq queue;
the strategy library module is used for appointing a vhost to receive corresponding queue data through the second rubbidmq queue, and carrying out real-time information pushing through the channel module based on an event model after event and attribute screening and combining with anti-disturbing pushing limitation;
and the channel module is used for establishing connection with each channel and event acquisition capability and providing event data sources for the data source module.
In a third aspect of the embodiments of the present disclosure,
provided is an information push system including:
the first unit is used for constructing an event model corresponding to the real-time event data based on the acquired real-time event data, analyzing the event model and determining event attributes and user behavior types corresponding to the real-time event data;
The second unit is used for carrying out unique matching on the event attribute and the user behavior type and preset rules in an event library, if the event attribute and the user behavior type are matched with the preset rules, preference information of the user is determined according to the user behavior type and the user history information, and state information of the user is determined according to the event attribute and the user history information, wherein the event library is used for receiving the real-time event data and carrying out unique matching on the real-time event data and the preset rules;
and the third unit is used for determining the scene where the user is based on the preference information and the state information and combining the real-time event data, and pushing information according to the trigger tag conditions corresponding to the scene where the user is, wherein the trigger tag conditions are used for indicating that the corresponding tag content is pushed under the condition that the trigger rule is met.
In a fourth aspect of embodiments of the present disclosure,
there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fifth aspect of embodiments of the present disclosure,
There is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
According to the method, based on the acquired real-time event data, the event attribute and the user behavior type corresponding to the real-time event data are determined, and the event attribute and the user behavior type are uniquely matched with the preset rules in the event library, so that the accuracy of information pushing content can be improved;
and distributing corresponding weight values aiming at the event attribute and the user behavior type, integrating the advantages of the subjective weight and the objective weight, finding a proper balance point between the two methods, and improving the objective accuracy of weight assignment and the priority of important index evaluation.
The method realizes that data above ten thousand levels are calculated in a dynamic configuration mode within a few seconds, can calculate the tag in real time based on the historical data of ten trillion levels, completes real-time data processing and real-time tag calculation, realizes the rapid identification of the requirements of users, and provides targeted marketing services.
The system disclosed by the invention uses the link and the rubbitmq to realize real-time calculation and pushing, the overall scheme reduces the coupling between the systems, and the intermediate storage is reduced to truly realize real-time event marketing.
The channel module adapts to the difference of various channel systems, establishes connection with various channels and event acquisition capability, registers service and control route of various application systems through a unified central node, avoids point-to-point tight coupling connection between the systems, and realizes efficient development and implementation and message processing.
The strategy library module supports the establishment of a unified strategy system, can construct strategies according to different application scenes, marketing targets and strategy groups, and deposits past successful marketing experience to form reusable strategy assets.
The atomic strategy and the process canvas support more complex comprehensive marketing scenes, and the automatic process of online marketing can be fast realized through a dragging mode, so that the efficiency of marketing activities is improved. And all the current marketing activities are displayed in the form of an execution calendar, so that the activation and deactivation operation of the marketing activities on the execution calendar is supported, and the marketing process is clearer and simpler.
Drawings
Fig. 1 is a flowchart illustrating a method for pushing information based on a user real-time event according to an embodiment of the disclosure.
Fig. 2 is a schematic structural diagram of an information push system based on a user real-time event according to an embodiment of the disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present disclosure, the size of the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in this disclosure, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this disclosure, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in this disclosure, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a from which B may be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the present disclosure is described in detail below with specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a flow chart of an information pushing method based on a user real-time event according to an embodiment of the disclosure, as shown in fig. 1, the method includes:
s101, constructing an event model corresponding to real-time event data based on the acquired real-time event data, analyzing the event model, and determining event attributes and user behavior types corresponding to the real-time event data;
by way of example, the real-time event data may include marketing-related transaction, customer, product, etc. data, such as, for example, the target customer transacting at the current time node, the customer's ID, and product data purchased by the customer, etc.; in addition, the real-time event data may include the behavior of the user in the operating application system, such as shopping for merchandise, browsing activities, browsing merchandise, sharing activities, etc., with different applications having different behaviors.
The event model corresponding to the real-time event data is used for carrying out mathematical abstraction on the real-time event data, extracting the characteristics corresponding to the real-time event data, carrying out rule matching on the extracted characteristics and an event library, and simultaneously providing a basic data source for the event library. The event model may include an existing neural network model, and the comparison of the embodiment of the present disclosure is not repeated.
The event attribute corresponding to the real-time event data can comprise data such as event ID, event name, event occurrence time, event classification ID, event source and the like, and can also be used for creating corresponding expansion attribute of each event in a self-defined manner according to the current event attribute, such as adding attribute information of a specific event and the event occurring in a specific scene.
The user behavior types may include behavior types that match the user portraits, which may include, for example, the user portraits being a single young female, and occasional behavior types, which may include purchasing tide plays, travel tickets for country, etc.; the contingent behavioral types may include purchase of maternal and infant products such as milk powder, infant products, and the like.
S102, carrying out unique matching on the event attribute and the user behavior type and preset rules in an event library, if so, determining preference information of the user according to the user behavior type and the user history information, and determining state information of the user according to the event attribute and the user history information;
illustratively, the event attributes include a first attribute for indicating an attribute having an association with the user tag greater than an association threshold and a second attribute for indicating an attribute having an association with the user tag less than the association threshold; the user behavior types comprise a first behavior type and a second behavior type, wherein the first behavior type is used for indicating the behavior type matched with the user tag, and the second behavior type is used for indicating the accidental behavior type;
the relevance of the event attribute and the user label can be determined by calculating the spatial distance between the vector corresponding to the event attribute and the vector corresponding to the user label, and determining whether the event attribute is a first attribute or a second attribute by comparing the spatial distance with a preset relevance threshold.
For example, the preference information of the user may be determined according to the user behavior type and the history information of the user, specifically, if the label corresponding to the user is a pregnant woman, the user behavior type is a first behavior type, that is, a behavior type matched with the user label, and in combination with the historical purchase record of the user in the past 30 days, for example, the user purchases a maternal product in the past for more than 500 yuan, the preference information of the user may be determined to be a preferred maternal product;
For example, the state information of the user may be determined according to the event attribute and the history information of the user, specifically, if the event attribute is an attribute that the association with the label of the user is greater than the association threshold, or taking the label corresponding to the user as an example of pregnant woman, the event attribute is a purchase of the crib, and in combination with the history purchase record of the user in the past 30 days, for example, the user purchases a maternal and infant product in the past more than 500 yuan, the state information of the user may be determined as the user with higher consumption willingness.
In an alternative embodiment of the present invention,
the uniquely matching the event attribute, the user behavior type and the preset rule in the event library comprises the following steps:
respectively distributing corresponding weight values for the event attribute and the user behavior type, wherein if the event attribute is a first attribute, a first weight value is distributed for the event attribute, and if the event attribute is a second attribute, a second weight value is distributed for the event attribute, and the first weight value is larger than the second weight value;
if the user behavior type is the first behavior type, a third weight value is allocated to the user behavior type, and if the user behavior type is the second behavior type, a fourth weight value is allocated to the user behavior type, wherein the third weight value is larger than the fourth weight value;
Determining a rule characteristic value corresponding to the real-time event data according to the weight value, the event attribute and the user behavior type;
and calculating a plurality of space distances from the preset rules in the event library based on the rule characteristic values, and taking the preset rule with the smallest distance in the plurality of space distances as the rule uniquely matched with the real-time event data.
The event attribute is used for indicating the occurrence of an event, and the user behavior is used for indicating the behavior type related to the user portrait, so that different weight values are allocated to the event attribute and the user behavior type, the emphasis of the event attribute and the user behavior type on the last information push can be highlighted, and the accuracy and pertinence of the information push are improved.
The first attribute is used for indicating the attribute with the relevance to the user tag being larger than the relevance threshold value, and the second attribute is used for indicating the attribute with the relevance to the user tag being smaller than the relevance threshold value; an attribute above the association threshold indicates a strong association with a given user tag for which the duty cycle should be enhanced to make the final result more accurate; correspondingly, the second weight value is smaller than the first weight value, so that the duty ratio of the second attribute in subsequent calculation is reduced, the calculation pressure is balanced, and the pushing result is more targeted. The first weight value and the second weight value change according to the magnitudes of the first attribute value and the second attribute value.
The third weight value and the fourth weight value are the same, and are not described in detail herein.
Determining a rule characteristic value corresponding to the real-time event data according to the weight value, the event attribute and the user behavior type;
calculating a plurality of space distances from a preset rule in the event library based on the rule characteristic value, and taking the preset rule with the smallest distance in the plurality of space distances as a rule uniquely matched with the real-time event data;
illustratively, the rule characteristic value of an embodiment of the present disclosure may be expressed as g= [ Q ] i ,S j ,Y j ]
Where j e [1,2,3,4], j e [1,2], qi represents the ith weight value, sj represents the jth event attribute, yj represents the jth user behavior type.
The method for calculating a plurality of spatial distances from a preset rule in the event library based on the rule characteristic value comprises the following steps:
the spatial distance is calculated according to the following formula:
wherein Gxm represents a preset rule Gx in the mth event library.
And taking the preset rule with the smallest distance in the plurality of space distances as the rule which is uniquely matched with the real-time event data, wherein the prediction rule with the smallest distance in the space distances represents the rule which is closest to the real-time event data, so that the rule which is most matched with the real-time event data can be selected from the plurality of preset rules in the event database, and the accuracy of information pushing is improved.
In an alternative embodiment of the present invention,
the method for allocating the corresponding weight value to the event attribute and the user behavior type comprises the following steps:
respectively distributing corresponding evaluation indexes for the event attribute and the user behavior type, and constructing a corresponding evaluation index matrix;
based on the evaluation index matrixes, respectively endowing each evaluation index matrix with a corresponding evaluation weight vector through a mixed weighting strategy;
and distributing corresponding evaluation indexes for the event attribute and the user behavior type through the evaluation weight vector, determining a weight balance point based on a preset target gain function, and distributing corresponding weight values for the event attribute and the user behavior type according to the determined weight balance point and the evaluation weight vector.
Corresponding evaluation indexes are distributed aiming at different evaluation objects, corresponding weight values are determined by integrating a plurality of factors, uncertainty of various factors can be quantified, the weight values are aggregated, and an optimal solution is ensured.
Illustratively, the evaluation index matrix may be expressed as:
wherein Xij represents an evaluation index matrix, and represents a contribution value of an ith evaluation index in a jth evaluation object, wherein the contribution value can be represented as a duty ratio of a single evaluation index in all evaluation indexes of a certain evaluation object; for indicating the accuracy of the evaluation index on the evaluation object.
In an alternative embodiment of the present invention,
the assigning the corresponding evaluation weight vector to each evaluation index matrix through the mixed weighting strategy based on the evaluation index matrix comprises the following steps:
wherein W represents an evaluation weight vector, M, N represents the number of evaluation indexes and the number of evaluation objects respectively, wherein the evaluation objects comprise the event attribute and the user behavior type, a and b represent a first weight duty ratio and a second weight duty ratio respectively, W1 and W2 represent a first strategy weighting matrix and a second strategy weighting matrix respectively, xij represents an evaluation index matrix, and represents a contribution value of an ith evaluation index at a jth evaluation object.
The step of distributing corresponding evaluation indexes for the event attribute and the user behavior type through the evaluation weight vector, determining a weight balance point based on a preset target gain function, and distributing corresponding weight values for the event attribute and the user behavior type according to the determined weight balance point and the evaluation weight vector comprises the following steps:
the method for determining the weight balance point can be as follows:
wherein the weight balance point is the minimum value of minL, f i (W) represents a target yield function, xij represents an evaluation index matrix, Z represents the number of loop iterations, u represents a resolution coefficient, and Δk represents the closeness.
The assignment of the corresponding weight values may be as follows:
Q＝minL*W
and setting a combined weight value according to the weight balance point and the evaluation weight vector.
S103, determining a scene where a user is based on the preference information and the state information and combining the real-time event data, and pushing information according to a trigger tag condition corresponding to the scene where the user is, wherein the trigger tag condition is used for pushing corresponding tag content under the condition that a trigger rule is met.
The tag is an exemplary bridge connecting the user, the scene and the event, the scene where the user is located is determined by combining the preference information and the state information of the user and the real-time event data, and information pushing is performed according to the trigger tag condition corresponding to the scene where the user is located.
In an alternative embodiment of the present invention,
the determining the scene of the user based on the preference information and the state information in combination with the real-time event data, and pushing the information according to the trigger tag condition corresponding to the scene of the user comprises the following steps:
Extracting preference features from the preference information and extracting state features from the state information respectively, and taking the preference features and the state features as user features;
extracting a plurality of event features from the real-time event data, and calculating the similarity of any two event features in the plurality of event features, wherein the event features with the similarity larger than a preset similarity threshold value are used as an initial event feature set;
and determining the association degree of the association event feature set and the scene where the user is located, judging whether the event feature with the highest association degree accords with the trigger tag condition, and pushing information if so, wherein the association event feature set is used for indicating event features of an intersection set generated between the initial event feature set and the scene where the user is located.
Illustratively, the calculating the similarity of any two event features of the plurality of event features of the embodiments of the present disclosure may be calculated with reference to a pearson correlation coefficient method using a variation of cosine similarity. The rate of growth of events tends to be slower than the user's increase, and orders of magnitude smaller than the user, so by considering the similarity between events from the user's feature point of view, there is often a faster recommendation rate.
In an alternative embodiment of the present invention,
the triggering label conditions comprise a first triggering condition and a second triggering condition, wherein the first triggering condition is used for indicating that the preference information and the label corresponding to the state information are triggered in real time when being matched with the prestored label in the event library, the second triggering condition is used for indicating that the preference information and the label corresponding to the state information are triggered in delayed mode when being not matched with the prestored label in the event library,
the step of pushing information according to the trigger tag conditions corresponding to the scene where the user is located comprises the following steps:
if the triggering tag condition is a first triggering condition, information pushing is performed in real time;
and if the trigger tag condition is a second trigger condition, carrying out information push in a delayed manner.
The triggering tag conditions comprise a first triggering condition and a second triggering condition, wherein the first triggering condition is real-time triggering, and real-time calculation can be performed according to real-time behavior data of a user in a real-time triggering mode, so that the requirement of the user can be rapidly identified, and information pushing can be performed in real time;
the second triggering condition is delay triggering, that is, the preference information and the label corresponding to the state information are not matched with the prestored label in the event library, for example, the user is pregnant woman, but according to the fact that the user accidentally searches a heavy locomotive and is not matched with the prestored label in the event library, delay triggering can be considered, message missending can be avoided, and various requirements of the user can be covered.
In a second aspect of the embodiments of the present disclosure,
providing a user real-time event based information push system applying the user real-time event based information push method of any one of the foregoing, fig. 2 is a schematic structural diagram of a user real-time event based information push system according to an embodiment of the present disclosure, as shown in fig. 2, where the system includes:
the data source module is used for collecting original real-time event data through the collector and pushing the data to the first rubbidq queue to provide basic data sources for the event library module;
the event library module is used for processing the real-time event data through the link according to the basic attribute and the expansion attribute, checking whether the attribute is consistent with a preset rule, and pushing the real-time event data to a second rabkitmq queue;
the strategy library module is used for appointing a vhost to receive corresponding queue data through the second rubbidmq queue, and carrying out real-time information pushing through the channel module based on an event model after event and attribute screening and combining with anti-disturbing pushing limitation;
and the channel module is used for establishing connection with each channel and event acquisition capability and providing event data sources for the data source module.
Illustratively, a flank is a distributed processing engine oriented to distributed streaming data and batch data that is capable of providing both streaming (DataStreams) and batch (DataSet) types of functionality.
The Flink supports exact-Once, thereby ensuring that each message is consumed and only Once. The message passing property exact-Once of the link is one implementation of a distributed snapshot paper based on Chandy and lamort. The user can customize the Checkpoint interval time, a special snapshot marker message (barrer) will be inserted into all data sources at regular time, barrer flows with other data messages in the directed acyclic graph, but the user defined business logic will not process it because the storage of snapshots is asynchronous and incremental operation so that the processing of data messages is not blocked. If abnormal conditions such as node crash occur, only the last successfully saved distributed snapshot state needs to be restored.
In an alternative embodiment of the present invention,
the atomic strategy establishes an event model through event and attribute screening, the event model needs to be configured with triggered event and attribute screening rules corresponding to the event, and targeted information pushing is configured aiming at the event model.
In an alternative embodiment of the present invention,
the process canvas displays all current activity information in the form of an execution calendar through a drag and drop form online automatic process, and supports the activation and deactivation operation of the activity on the execution calendar.
The data source module is responsible for accessing real-time data of all event marketing related transactions, clients, products and the like, collecting original event data through the collector, pushing the original event data to the rubbitmq, and providing basic data sources for the event library module.
By way of example only, and in an illustrative,
the event library module defines event and attribute rules (including basic attributes such as event ID, event name, event occurrence time, event classification ID, event source, client ID and the like, and creates corresponding expansion attributes of each event in a self-defining way) about the aspects such as user who is, occurrence time, occurrence place, specific content and the like. And then, abstracting a data model based on the accessed real-time data, and establishing a corresponding relation between the data model and the rule.
The event library uses the flink and the rubbitmq to realize real-time calculation and pushing of mass data. The event task can be quickly configured for one-key submission, the flink processes the event data according to the basic attribute and the expansion attribute, and after checking whether the attribute is consistent with the rule, the event data is pushed to a downstream rubbitmq queue. Wherein message middleware rubbitmq ensures that the data is accurate once by enabling unique values to interwork with checkpoints of flink. The whole module starts a rubbitmq dead message queue for checking abnormal data, and a yarn deployment mode is adopted to facilitate user migration. The resource management is flexible, and the capacity expansion and operation and maintenance are convenient.
The event library module can automatically maintain the change of the attribute according to the event trigger of various channels, receive the event data in real time, process the event, check the event attribute, update the state of the client object, and is used for supporting the accurate control of which time point in the marketing process to promote information to the client.
Optionally, the policy library module supports building a unified policy system, and can build policies according to different application scenarios, marketing targets and policy groups, precipitate past successful marketing experiences and form reusable policy assets. The method realizes unified contact channel, marketing content, marketing plan and other marketing assets, establishes a marketing strategy library, and can meet the requirement of converting the marketing strategy with excellent effect into a template with parameters for quick copying or adjustment.
Marketing strategies are divided into two types of 'atomic strategies' and 'flow canvas' according to the complexity degree of marketing scenes:
the atomic strategy supports independent and finer granularity marketing scenes, an event model (such as a client behavior model, a client transaction model, a product state model and the like) is established through event and attribute screening, the event model needs to be configured with triggered event and attribute screening rules corresponding to the event, targeted marketing asset pushing is configured for the event model, and marketing assets include but are not limited to: talk, marketing campaigns, rights and products.
The process canvas supports a more complex comprehensive marketing scene, and can rapidly line up the automatic marketing process in a dragging mode, so that the efficiency of marketing activities is improved. The comprehensive marketing scene often comprises a plurality of atomic strategies, and the relevance and state change among the atomic strategies can be comprehensively considered through a flow canvas, so that timely and accurate information pushing is given to customers. And all the current marketing activities are displayed in the form of an execution calendar, so that the activation and deactivation operation of the marketing activities on the execution calendar is supported, and the marketing process is clearer and simpler.
The marketing strategy module supports the anti-disturbing function at the same time, can self-define the pushing limit of the same user in the atomic strategy, the flow canvas or the global scope, namely the maximum pushing times of the same user in the period, controls the marketing frequency of the real-time event marketing to the same user, realizes the promotion of user experience, prevents disturbing the user and maximizes the marketing effect.
The strategy library module receives the corresponding queue data through the rubbitmq appointed vhost, screens event and attribute event models in an atomic strategy or a flow canvas, combines anti-disturbing push limit, and pushes marketing assets in real time through the channel module.
In an alternative embodiment of the present invention,
channels should include, but are not limited to: intelligent outbound, application messages, short messages, template messages, and customer relationship management systems. The channel module adapts to the differences of various channel systems, establishes connection with various channels and event acquisition capability, and provides event data sources for the data source module. The related channel systems and transaction interfaces are more in types, the module is supported by the basic framework of a standardized service communication bus and an integrated service platform, and registers the service and control route of each application system through a unified central node, so that point-to-point tight coupling connection among the systems is avoided, and efficient development and implementation and message processing are realized.
In a third aspect of the embodiments of the present disclosure,
provided is an information push system including:
the first unit is used for constructing an event model corresponding to the real-time event data based on the acquired real-time event data, analyzing the event model and determining event attributes and user behavior types corresponding to the real-time event data;
the second unit is used for carrying out unique matching on the event attribute and the user behavior type and preset rules in an event library, if the event attribute and the user behavior type are matched with the preset rules, preference information of the user is determined according to the user behavior type and the user history information, and state information of the user is determined according to the event attribute and the user history information, wherein the event library is used for receiving the real-time event data and carrying out unique matching on the real-time event data and the preset rules;
And the third unit is used for determining the scene where the user is based on the preference information and the state information and combining the real-time event data, and pushing information according to the trigger tag conditions corresponding to the scene where the user is, wherein the trigger tag conditions are used for indicating that the corresponding tag content is pushed under the condition that the trigger rule is met.
In a fourth aspect of embodiments of the present disclosure,
there is provided an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fifth aspect of embodiments of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Note that all features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic set of equivalent or similar features. Where used, further, preferably, still further and preferably, the brief description of the other embodiment is provided on the basis of the foregoing embodiment, and further, preferably, further or more preferably, the combination of the contents of the rear band with the foregoing embodiment is provided as a complete construct of the other embodiment. A further embodiment is composed of several further, preferably, still further or preferably arrangements of the strips after the same embodiment, which may be combined arbitrarily.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are by way of example only and are not limiting. The objects of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been shown and described in the examples and embodiments of the invention may be modified or practiced without departing from the principles described.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present disclosure.
Claims (9)
1. The information pushing method based on the user real-time event is characterized by comprising the following steps of:
based on the acquired real-time event data, constructing an event model corresponding to the real-time event data, analyzing the event model, and determining event attributes and user behavior types corresponding to the real-time event data;
the event attribute and the user behavior type are subjected to unique matching with preset rules in an event library, if the event attribute and the user behavior type are matched with the preset rules, preference information of the user is determined according to the user behavior type and the user history information, and state information of the user is determined according to the event attribute and the user history information, wherein the event library is used for receiving the real-time event data and carrying out unique matching on the real-time event data and the preset rules;
Based on the preference information and the state information, combining the real-time event data, determining a scene where a user is located, and pushing information according to a trigger tag condition corresponding to the scene where the user is located, wherein the trigger tag condition is used for pushing corresponding tag content under the condition that a trigger rule is indicated to be met;
the event attributes comprise a first attribute and a second attribute, wherein the first attribute is used for indicating the attribute that the association with the user tag is larger than an association threshold value, and the second attribute is used for indicating the attribute that the association with the user tag is smaller than the association threshold value; the user behavior types comprise a first behavior type and a second behavior type, wherein the first behavior type is used for indicating the behavior type matched with the user tag, and the second behavior type is used for indicating the accidental behavior type;
the uniquely matching the event attribute, the user behavior type and the preset rule in the event library comprises the following steps:
respectively distributing corresponding weight values for the event attribute and the user behavior type, wherein if the event attribute is a first attribute, a first weight value is distributed for the event attribute, and if the event attribute is a second attribute, a second weight value is distributed for the event attribute, and the first weight value is larger than the second weight value;
If the user behavior type is the first behavior type, a third weight value is allocated to the user behavior type, and if the user behavior type is the second behavior type, a fourth weight value is allocated to the user behavior type, wherein the third weight value is larger than the fourth weight value;
determining a rule characteristic value corresponding to the real-time event data according to the weight value, the event attribute and the user behavior type;
and calculating a plurality of space distances from the preset rules in the event library based on the rule characteristic values, and taking the preset rule with the smallest distance in the plurality of space distances as the rule uniquely matched with the real-time event data.
2. The method of claim 1, wherein the method of assigning the event attribute and the user behavior type with corresponding weight values comprises:
respectively distributing corresponding evaluation indexes for the event attribute and the user behavior type, and constructing a corresponding evaluation index matrix;
based on the evaluation index matrixes, respectively endowing each evaluation index matrix with a corresponding evaluation weight vector through a mixed weighting strategy;
and distributing corresponding evaluation indexes for the event attribute and the user behavior type through the evaluation weight vector, determining a weight balance point based on a preset target gain function, and distributing corresponding weight values for the event attribute and the user behavior type according to the determined weight balance point and the evaluation weight vector.
3. The method according to claim 2, wherein assigning each evaluation index matrix a corresponding evaluation weight vector by a mixed weighting strategy based on the evaluation index matrix comprises:
wherein W represents an evaluation weight vector, M, N represents the number of evaluation indexes and the number of evaluation objects respectively, wherein the evaluation objects comprise the event attribute and the user behavior type, a and b represent a first weight duty ratio and a second weight duty ratio respectively, W1 and W2 represent a first strategy weighting matrix and a second strategy weighting matrix respectively, xij represents an evaluation index matrix, and represents a contribution value of an ith evaluation index at a jth evaluation object.
4. The method of claim 1, wherein the determining a scene in which the user is located based on the preference information and the status information in combination with the real-time event data, and performing information pushing according to a trigger tag condition corresponding to the scene in which the user is located comprises:
extracting preference features from the preference information and extracting state features from the state information respectively, and taking the preference features and the state features as user features;
Extracting a plurality of event features from the real-time event data, and calculating the similarity of any two event features in the plurality of event features, wherein the event features with the similarity larger than a preset similarity threshold value are used as an initial event feature set;
and determining the association degree of the association event feature set and the scene where the user is located, judging whether the event feature with the highest association degree accords with the trigger tag condition, and pushing information if so, wherein the association event feature set is used for indicating event features of an intersection set generated between the initial event feature set and the scene where the user is located.
5. The method of claim 1, wherein the trigger tag conditions include a first trigger condition for indicating that the preference information and the tag corresponding to the status information match the pre-stored tag in the event library in real time and a second trigger condition for indicating that the preference information and the tag corresponding to the status information do not match the pre-stored tag in the event library in time delay,
the step of pushing information according to the trigger tag conditions corresponding to the scene where the user is located comprises the following steps:
If the triggering tag condition is a first triggering condition, information pushing is performed in real time;
and if the trigger tag condition is a second trigger condition, carrying out information push in a delayed manner.
6. A user real-time event based information push system applying the user real-time event based information push method of any of claims 1 to 5, characterized in that the system comprises:
the data source module is used for collecting original real-time event data through the collector and pushing the data to the first rubbidq queue to provide basic data sources for the event library module;
the event library module is used for processing the real-time event data through the link according to the basic attribute and the expansion attribute, checking whether the attribute is consistent with a preset rule, and pushing the real-time event data to a second rabkitmq queue;
the strategy library module is used for appointing a vhost to receive corresponding queue data through the second rubbidmq queue, and carrying out real-time information pushing through the channel module based on an event model after event and attribute screening and combining with anti-disturbing pushing limitation;
and the channel module is used for establishing connection with each channel and event acquisition capability and providing event data sources for the data source module.
7. An information push system, comprising:
the first unit is used for constructing an event model corresponding to the real-time event data based on the acquired real-time event data, analyzing the event model and determining event attributes and user behavior types corresponding to the real-time event data;
the second unit is used for carrying out unique matching on the event attribute and the user behavior type and preset rules in an event library, if the event attribute and the user behavior type are matched with the preset rules, preference information of the user is determined according to the user behavior type and the user history information, and state information of the user is determined according to the event attribute and the user history information, wherein the event library is used for receiving the real-time event data and carrying out unique matching on the real-time event data and the preset rules;
a third unit, configured to determine, based on the preference information and the status information, a scene where the user is located in combination with the real-time event data, and perform information pushing according to a trigger tag condition corresponding to the scene where the user is located, where the trigger tag condition is used to instruct that corresponding tag content is pushed when a trigger rule is satisfied;
The event attributes comprise a first attribute and a second attribute, wherein the first attribute is used for indicating the attribute that the association with the user tag is larger than an association threshold value, and the second attribute is used for indicating the attribute that the association with the user tag is smaller than the association threshold value; the user behavior types comprise a first behavior type and a second behavior type, wherein the first behavior type is used for indicating the behavior type matched with the user tag, and the second behavior type is used for indicating the accidental behavior type;
the uniquely matching the event attribute, the user behavior type and the preset rule in the event library comprises the following steps:
respectively distributing corresponding weight values for the event attribute and the user behavior type, wherein if the event attribute is a first attribute, a first weight value is distributed for the event attribute, and if the event attribute is a second attribute, a second weight value is distributed for the event attribute, and the first weight value is larger than the second weight value;
if the user behavior type is the first behavior type, a third weight value is allocated to the user behavior type, and if the user behavior type is the second behavior type, a fourth weight value is allocated to the user behavior type, wherein the third weight value is larger than the fourth weight value;
Determining a rule characteristic value corresponding to the real-time event data according to the weight value, the event attribute and the user behavior type;
and calculating a plurality of space distances from the preset rules in the event library based on the rule characteristic values, and taking the preset rule with the smallest distance in the plurality of space distances as the rule uniquely matched with the real-time event data.
8. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 5.
9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310036987.1A CN115983902B (en) | 2023-01-10 | 2023-01-10 | Information pushing method and system based on user real-time event |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310036987.1A CN115983902B (en) | 2023-01-10 | 2023-01-10 | Information pushing method and system based on user real-time event |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115983902A CN115983902A (en) | 2023-04-18 |
| CN115983902B true CN115983902B (en) | 2023-10-20 |
Family
ID=85959548
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310036987.1A Active CN115983902B (en) | 2023-01-10 | 2023-01-10 | Information pushing method and system based on user real-time event |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115983902B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112561603A (en) * | 2020-12-25 | 2021-03-26 | 杭州云徙科技有限公司 | Event label implementation method and system based on real-time user behaviors |
| CN113065894A (en) * | 2021-03-24 | 2021-07-02 | 天天惠民(北京)智能物流科技有限公司 | Data collection method and device based on user portrait and order analysis and storage medium |
| WO2022100518A1 (en) * | 2020-11-12 | 2022-05-19 | 北京沃东天骏信息技术有限公司 | User profile-based object recommendation method and device |
| CN115002200A (en) * | 2022-05-31 | 2022-09-02 | 平安银行股份有限公司 | User portrait based message pushing method, device, equipment and storage medium |
-
2023
- 2023-01-10 CN CN202310036987.1A patent/CN115983902B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022100518A1 (en) * | 2020-11-12 | 2022-05-19 | 北京沃东天骏信息技术有限公司 | User profile-based object recommendation method and device |
| CN112561603A (en) * | 2020-12-25 | 2021-03-26 | 杭州云徙科技有限公司 | Event label implementation method and system based on real-time user behaviors |
| CN113065894A (en) * | 2021-03-24 | 2021-07-02 | 天天惠民(北京)智能物流科技有限公司 | Data collection method and device based on user portrait and order analysis and storage medium |
| CN115002200A (en) * | 2022-05-31 | 2022-09-02 | 平安银行股份有限公司 | User portrait based message pushing method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115983902A (en) | 2023-04-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10748193B2 (en) | Assessing probability of winning an in-flight deal for different price points | |
| CN111507543B (en) | Model training method and device for predicting business relation between entities | |
| CN111737546B (en) | Method and device for determining entity service attribute | |
| US11257110B2 (en) | Augmenting missing values in historical or market data for deals | |
| US20190179927A1 (en) | Enterprise data services cockpit | |
| CN110717597A (en) | Method and device for acquiring time sequence characteristics by using machine learning model | |
| US10902446B2 (en) | Top-down pricing of a complex service deal | |
| CN113205402A (en) | Account checking method and device, electronic equipment and computer readable medium | |
| CN114943279A (en) | Method, device and system for predicting bidding cooperative relationship | |
| CN114282011A (en) | Knowledge graph construction method and device, and graph calculation method and device | |
| CN109829593B (en) | Credit determining method and device for target object, storage medium and electronic device | |
| US20210142406A1 (en) | Vehicle selection platform | |
| CN114036159A (en) | Bank business information updating method and system | |
| Srinivasa et al. | Network Data Analytics | |
| CN115983902B (en) | Information pushing method and system based on user real-time event | |
| CN110795638A (en) | Method and apparatus for outputting information | |
| CN116361542A (en) | Product recommendation method, device, computer equipment and storage medium | |
| CN111476511B (en) | Data display method and device for specific risks faced by server | |
| CN114897607A (en) | Data processing method and device for product resources, electronic equipment and storage medium | |
| CN115130756A (en) | Online service management method and device, electronic equipment and storage medium | |
| CN114493686A (en) | Operation content generation and pushing method and device | |
| US20200286104A1 (en) | Platform for In-Memory Analysis of Network Data Applied to Profitability Modeling with Current Market Information | |
| CN113850669A (en) | User grouping method and device, computer equipment and computer readable storage medium | |
| CN112328899A (en) | Information processing method, information processing apparatus, storage medium, and electronic device | |
| US20230128246A1 (en) | Collaborative electronic platform for predicting non-payments for companies and associated method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |