CA3150183C

CA3150183C - Flink streaming processing engine method and device for real-time recommendation and computer equipment

Info

Publication number: CA3150183C
Application number: CA3150183A
Authority: CA
Inventors: Xiaoming He; Rui Zhou
Original assignee: 10353744 Canada Ltd
Current assignee: 10353744 Canada Ltd
Priority date: 2021-02-25
Filing date: 2022-02-25
Publication date: 2024-02-27
Anticipated expiration: 2042-02-25
Also published as: CA3150183A1; CN112948450B; CN112948450A

Abstract

Pertaining to the field of big data recommendation technology, the present application discloses a Flink stream processing engine method for real-time recommendation, and corresponding device and computer equipment. The method comprises: performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short- term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance; reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue; performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

Description

FLINK STREAMING PROCESSING ENGINE METHOD AND DEVICE FOR REAL-TIME RECOMMENDATION AND COMPUTER EQUIPMENT
BACKGROUND OF THE INVENTION
Technical Field [0001] The present application relates to the field of big data recommendation technology, and more particularly to a Flink stream processing engine method for real-time recommendation, and corresponding device and computer equipment.
Description of Related Art

[0002] With the upgrading of user experiences, higher and higher demand is correspondingly put on the real timeliness requirement of recommendation. The traditional double-layered recommendation framework (application layer, offline layer) engenders the occurrence of such phenomena as complicated function of the application layer, response timeout and data inconsistency. In addition, although realization of recommendation at the offline layer is quick, time precision of recommendation reaches only to the magnitude of days;
however, since user interests generally diminish rapidly with the attenuation of time, if items of interest to users cannot be pushed to the users at the first time, the conversion rate would be reduced and even user loss ensues. This makes extremely valuable the creation of a set of real-time recommending system capable of analyzing users' behaviors in real time and so performing prediction that the system can respond timely.

[0003] Real-time recommendation means the capabilities to perceive changes in users' behaviors and to push precise contents to users according to users' behaviors generated by the users in real time. At present, the typical recommending system is a triple-layered framework, namely online, near-line and offline. By means of offline or near-line model training, near-line real-time feature engineering and callback and sorting, and finally through Date Recue/Date Received 2022-03-22 forced interference and bottom coverage of the business performed at the online layer, the value of the system is fully reflected in terms of highly speedy response to scenarios with massive data volumes.

[0004] The recommendation algorithm engine that is based on stream processing is at the kernel position of the near-line portion, whereas the traditional scheme that employs STORM or SPARKSTREAMING, for example, for realization is problematic in the following aspects:

[0005] 1) There lacks utilization of the latest users' behavior data, and the user feature model cannot be updated, so it is impossible to perceive in real time the real-time changes in users' interests.

[0006] 2) It is impossible to give considerations to reliability, disaster recovery, performance and generality at the same time, thereby making it difficult to deploy the algorithm model and to complete real-time response.

[0007] 3) There are too many technical details by the use of JAVA or PYTHON
language for development, whereby development is slow, and it is difficult to timely respond to business requirements.
SUMMARY OF THE INVENTION

[0008] In order to overcome at least one problem mentioned in the above Description of Related Art, the present application provides a Flink stream processing engine method for real-time recommendation, and corresponding device and computer equipment, with technical solutions being specified as follows.

[0009] According to the first aspect, there is provided a Flink stream processing engine method for real-time recommendation, which method comprises:

[0010] performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a Date Recue/Date Received 2022-03-22 distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;

[0011] reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0012] performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0013] writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0014] Further, prior to the step of performing short-term feature calculation on a log in a message queue according to a calculation model file, the method further comprises:

[0015] obtaining a configuration file that contains a configuration parameter;
and

[0016] loading from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

[0017] Further, the step of obtaining a configuration file includes:

[0018] synchronizing at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

[0019] Further, the step of loading from a model repository a model file to which the configuration parameter corresponds includes:

[0020] reading a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and

[0021] loading from the model repository a model file to which the model identification parameter corresponds.

Date Recue/Date Received 2022-03-22

[0022] Further, the step of performing short-term feature calculation on a log in a message queue according to a calculation model file includes:

[0023] performing short-term feature calculation on the log according to the calculation model file through the various distributed nodes;

[0024] the step of performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user includes:

[0025] generating a prediction model according to the prediction model file through the various distributed nodes; and

[0026] inputting the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

[0027] Further, the distributed storage layer includes a cache component and a persistence component, and the step of reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue includes:

[0028] reading a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;

[0029] reading a long-term feature and a short-term feature of the to-be-recommended user from the cache component, if reading from the local cache failed; and

[0030] reading a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, if reading from the cache component failed, and asynchronously writing the read features in the cache component.

[0031] Further, the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

Date Recue/Date Received 2022-03-22

[0032] According to the second aspect, there is provided a Flink stream processing engine device for real-time recommendation, which device comprises:

[0033] an algorithm calculating module, for performing short-term feature calculation on a log in a message queue according to a calculation model file;

[0034] a data pushing module, for storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance; and

[0035] a feature enquiring module, for reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0036] the algorithm calculating module is further employed for performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0037] the data pushing module is further employed for writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0038] Further, the device further comprises:

[0039] a configuration synchronizing module, for obtaining a configuration file that contains a configuration parameter; and

[0040] a model loading module, for loading from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

[0041] Further, the configuration synchronizing module is specifically employed for:

[0042] synchronizing at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.
Date Recue/Date Received 2022-03-22

[0043] Further, the model loading module is specifically employed for:

[0044] reading a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and

[0045] loading from the model repository a model file to which the model identification parameter corresponds.

[0046] Further, the algorithm calculating module is specifically employed for:

[0047] performing short-term feature calculation on the log according to the calculation model file through the various distributed nodes;

[0048] the algorithm calculating module is specifically further employed for:

[0049] generating a prediction model according to the prediction model file through the various distributed nodes; and

[0050] inputting the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

[0051] Further, the distributed storage layer includes a cache component and a persistence component, and the feature enquiring module is specifically employed for:

[0052] reading a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;

[0053] reading a long-term feature and a short-term feature of the to-be-recommended user from the cache component, if reading from the local cache failed; and

[0054] reading a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, if reading from the cache component failed, and asynchronously writing the read features in the cache component.

[0055] Further, the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

Date Recue/Date Received 2022-03-22

[0056] According to the third aspect, there is provided a computer equipment that comprises a memory, a processor and a computer program stored on the memory and operable on the processor, and the following operating steps are realized when the processor executes the computer program:

[0057] performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;

[0058] reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0059] performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0060] writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0061] According to the fourth aspect, there is provided a computer-readable storage medium storing a computer program thereon, and the following operating steps are realized when the computer program is executed by a processor:

[0062] performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;

[0063] reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0064] performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and Date Recue/Date Received 2022-03-22

[0065] writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0066] The technical solutions provided by the present application at least achieve the following advantageous effects:

[0067] 1. Message reliability guarantee and capacity expansion and extraction:
the distributed stream engine Flink itself brings about precise message reliability and flexible capacities to expand and extract, and makes it possible to reduce resource consumption in normal times but to increase resources in large-scale promotional activities to respond to peak flow.

[0068] 2. Reduction in algorithm development difficulty: the algorithm personnel more often employ such language systems as SQL, Python, and machine learning, rather than the JAVA language system, whereas the present application makes use of the SQL and P]\4IML
general language standards, whereby development difficulty and time consumption of the algorithm are both reduced, and most technical problems are solved.

[0069] 3. Reduction in response duration: the response duration by the use of an offline algorithm is usually of the hourly magnitude, and the operation time usually cannot be guaranteed, whereas the response time based on the use of the current engine is of the millisecond magnitude, whereby real-time recommendation and feature engineering are made possible.

[0070] 4. One-stop model deployment: models obtained by offline training can be stored in a general model repository, the current engine is subsequently specified to read the model of given coordinates before the engine automatically starts deployment, whereby massive deployment operations are dispensed with, and error and time cost are reduced.
BRIEF DESCRIPTION OF THE DRAWINGS

[0071] To more clearly describe the technical solutions in the embodiments of the present application, drawings required to be used in the description of the embodiments will be Date Recue/Date Received 2022-03-22 briefly introduced below. Apparently, the drawings introduced below are merely directed to some embodiments of the present application, while it is possible for persons ordinarily skilled in the art to acquire other drawings based on these drawings without spending creative effort in the process.

[0072] Fig. 1 is a view schematically illustrating data interaction between the stream processing engine and relevant components provided by the present application;

[0073] Fig. 2 is a view schematically illustrating the structure of the Flink stream processing engine provided by the present application;

[0074] Fig. 3 is a flowchart illustrating the Flink stream processing engine method for real-time recommendation provided by the present application;

[0075] Fig. 4 is a flowchart illustrating loading of the model file provided by the present application;

[0076] Fig. 5 is a flowchart illustrating reading of three-level cache features provided by the present application;

[0077] Fig. 6 is a view illustrating a cluster deployment framework provided by the present application; and

[0078] Fig. 7 is a view illustrating the internal structure of a computer equipment provided by an embodiment of the present application.
DETAILED DESCRIPTION OF THE INVENTION

[0079] To make more lucid and clear the objectives, technical solutions and advantages of the Date Recue/Date Received 2022-03-22 present application, the technical solutions in the embodiments of the present application will be more clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present application.
Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present application. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present application without spending creative effort shall all fall within the protection scope of the present application.

[0080] As should be noted, unless explicitly demanded otherwise in the context, such wordings as "comprising", "including" and "containing" as well as their various grammatical forms as used throughout the Description and the Claims shall be understood to indicate the meaning of inclusion rather than the meaning of exclusion or exhaustion, in other words, they collectively mean "including, but not limited to...". In addition, unless noted otherwise, the wordings of "more" and "plural" indicate the meaning of "two or more" in the description of the present application.

[0081] Fig. 1 is a view schematically illustrating data interaction between the stream processing engine and relevant components provided by the present application. As shown in Fig. 1, black line arrows indicate data flows, grey line arrows indicate file flows, and long-term features are pushed by offline data synchronizer (Long Term Feature Synchronizer) 101 to NOSQL storage layer 102 that at least contains a persistence component (Persistence) and a cache component (Cache). Stream processing engine (Stream Engine) 103 reads a configuration file from distributed configuration cluster (Configuration Zookeeper) 104, and loads from model repository (Model Repository) 105 a model file (for instance, SQL
model file and PMML model file) well trained in advance. Stream engine 103 uses a log in message queue (MQ Kafka) 106 to perform short-term feature engineering, calculates and thereafter stores data in the persistence component and the cache component in NOSQL storage layer 102 before writes back an event to message queue 106.
Thereafter, triggered by this event, predicting stream engine 103 obtains long-term features and short-Date Recue/Date Received 2022-03-22 term features from NOSQL storage layer 102, uses already loaded model text and script to perform real-time prediction, and finally writes the result in the persistence component and the cache component of NOSQL storage layer 102. A stream structure with separated reading and writing is thus formed. In actual application, with the use of the Flink component, the above stream processing is superior than Storm in terms of message supportability, volume of development, and throughput. The distributed configuration cluster is embodied as Zookeeper that can be combined with a Hadoop cluster.
The message queue is embodied as Kafica that is characteristic of high throughput and low time delay, and conforms to log-type, message-triggering scenarios. The persistence storage component is embodied as HBase that possesses distributed, high-writing and low-reading, large storage capacity, and column-extensible characteristics.
The cache component is embodied as Redis that possesses distributed, high-reading and high-writing, small storage capacity characteristics, and conforms to application scenarios.

[0082] Fig. 2 is a view schematically illustrating the structure of the Flink stream processing engine provided by the present application. As shown in Fig. 2, the Flink stream processing engine (Stream Engine) comprises the following modules: a configuration synchronizing module (Configuration Synchronizer), a model loading module (Model Loader), an MQ connecting module (MQ connector), a feature enquiring module (Query er), an algorithm calculating module (Algorithm Calculation), and a data pushing module (Data Pusher). Of these, the configuration synchronizing module, the model loading module and the MQ connecting module can be disposed on the same scheduler, and the feature enquiring module and the algorithm calculating module can be disposed on various distributed nodes in the distributed cluster, while the scheduler can perform information interaction with the various distributed nodes.

[0083] The configuration synchronizing module is employed for synchronizing various differently typed configuration files from the distributed configuration cluster, loading the same in the memory, and providing necessary parameter configurations for the Date Recue/Date Received 2022-03-22 recommending system, including, but not limited to, model hyperparameters, feature weights, and number restriction of recommendation results, etc., for example, selecting a corresponding recommendation algorithm for prediction from the model repository according to user configuration parameters, while it is further possible for the business personnel to interfere configurable recommendation results through the system configuration page to certain extent.

[0084] The model loading module is employed for loading SQL model files or PMML model files from the model repository, and sending the files to the various distributed nodes by means of the distributed file caching principle for standby use by distributed tasks.

[0085] The MQ connecting module is employed for obtaining log data from the message queue MQ so as to perform short-telin feature calculation through the algorithm calculating module, for obtaining an event from the message queue so as to facilitate the algorithm calculating module to base on the event to obtain long-term features and short-term features from the NOSQL storage layer, and additionally for writing the response event returned by the algorithm calculating module in the message queue MQ for use by downstream systems.

[0086] The feature enquiring module is employed for enquiring feature data from the NOSQL
cluster per piece or in batches. Due to the big data scenario, the requirement for reading feature data is short while reading in great batches and persistent. To meet such performance requirement, it is possible to employ the mode of a triple-layered cache plus a separated reading and writing mechanism.

[0087] The algorithm calculating module is located on the various distributed nodes, and uses the model file loaded in advance by the model loading module, after having received an event from the message queue, it uses the native stream SQL calculating function to calculate features or uses the PMML predicting function to predict results.

Date Recue/Date Received 2022-03-22

[0088] The data pushing module writes the resultant data in the persistence component and the cache component simultaneously, and writes the triggered event after obtaining response from the persistence component and the cache component in the message queue for downstream use.

[0089] In order to realize functional requirements of near-line streaming recommendation in a recommending system, and to give considerations at the same time to such problems as concerning message reliability, disaster recovery, performance, and transaction that possibly occur during the operating process of the recommending system, the present application ensures storage administration of global and local statuses and precise reliability guarantee through the distributed stream calculation framework Flink, performs prediction calculation by means of a general model repository client end and model files that conform to the SQL and PMML standards, realizes automatic model loading, and solves the problem in which it is difficult to deploy the algorithm model and it is difficult to respond in real time; quick reading and reliability of massive data are guaranteed through reading and writing separation and triple-layered caching mechanism of the feature data repository.

[0090] The technical solutions of the present application are further described below through a plurality of embodiments.

[0091] In one embodiment, there is provided a Flink stream processing engine method for real-time recommendation, with reference to Fig. 3, the method can comprise the following steps.

[0092] 301 ¨ performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term Date Recue/Date Received 2022-03-22 features obtained in advance.

[0093] The message queue is a mode of implementation of a message middleware, such as a kafca message queue, the message queue stores therein log data, the log data can include behavior data generated by user behavior operations within a preset time period, and the behavior operations are for instance browsing, clicking, listing as favorite, and sharing of a certain commodity object operated by a user on a certain APP page. The preset time period can be set according to practical requirements, for instance,1 day, 3 days or 7 days, etc., in addition, the present application makes no restriction to the source of the log data, and the log data can for example come from an application APP.

[0094] The distributed storage layer can be embodied as an NOSQL storage layer (a non-relational database) that includes a cache component and a persistence component, of which the cache component can be embodied as cache component Redis that possesses distributed, high-reading and high-writing, small storage capacity characteristics, and can store data within a period of time (such as within 1 year); the persistence storage component can be embodied as persistence storage component HBase that possesses distributed, high-writing and low-reading, large storage capacity, and column-extensible characteristics, and can store data that has been overdue longer (such as exceeding 1 year).
Both the cache component and the persistence component can be used to store long-term features and short-term features related to user operational behaviors.

[0095] The aforementioned long-term features are used to reflect long-term preference of a user with respect to a designated commodity, including user basic feature, user long-term behavior feature and other features, of which the user basic feature includes, for instance, age, gender, profession, place of residence and preference of the user; the user behavior feature means shopping behavior statistic values at an e-commerce platform within a period of time, such as the statistic feature of operational behaviors of the user under a certain commodity category within 30 days, 90 days, 180 days. Long-term features can Date Recue/Date Received 2022-03-22 be extracted out of user data, for example, a long-term model well trained through offline data is used to extract long-teini features of the user from user data, to which no specific restriction is made in the embodiments of the present application.

[0096] The aforementioned short-term features are used to reflect short-term preference of a user with respect to a designated commodity, and can be extracted and obtained from behavior operation data generated by the user within a preset time period, such as the behavior operation data generated within 7 days.

[0097] Specifically, the Flink stream processing engine can obtain log data from the message queue according to a preset time interval, employ a preset calculation model file to perform short-term feature calculation on the user behavior data in the log data, and store short-term features obtained by such calculation respectively in the cache component and the persistence component of the distributed storage layer. The preset time interval can be set according to practical requirements, such as 10 seconds. A machine learning algorithm can be employed to train primary data of a sample user to generate a short-term model, and to obtain a calculation model file of the short-term model. The machine learning algorithm can be embodied as an iterative decision tree, logic regression or a support vector machine, to which no restriction is made in the embodiments of the present application.

[0098] 302 ¨ reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue.

[0099] After the Flink stream processing engine has stored the short-term features of the user in the persistence component and the cache component in the distributed storage layer, an event will be written back to the message queue.

[0100] Specifically, having determined that the message queue has successfully received the Date Recue/Date Received 2022-03-22 event, triggered by this event, the Flink stream processing engine can obtain a long-term feature and a short-term feature of a to-be-recommended user from the cache component and the persistence component in the distributed storage layer. There may be one or more to-be-recommended user(s), to which no restriction is made in the embodiments of the present application.

[0101] The to-be-recommended user can be a user requiring to be recommended with interesting contents, the contents here can be commodity information, and the to-be-recommended user can be determined based on a preset user identification.

[0102] 303 ¨ performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user.

[0103] The preference prediction result of the to-be-recommended user can include commodity objects to be recommended.

[0104] Specifically, the Flink stream processing engine can generate a prediction model according to a prediction model file obtained in advance, and perform real-time prediction on the long-term feature and the short-term feature of the to-be-recommended user according to the prediction model to obtain the preference prediction result of the to-be-recommended user.

[0105] A machine learning algorithm can be employed to train to generate a prediction model and to obtain a prediction model file of the prediction model on the basis of the long-term feature and the short-term feature of a sample user and preference commodity objects of the sample user as training samples. The machine learning algorithm can be embodied as an iterative decision tree, logic regression or a support vector machine, to which no restriction is made in the embodiments of the present application.

Date Recue/Date Received 2022-03-22

[0106] In this embodiment, since the prediction model file is obtained by training and learning of the long-term feature and short-term feature of the user, long-term feature data of the user is not only used, but short-term (latest) feature data of the user is also given consideration, whereby it is made possible to more precisely predict preference commodity objects of the to-be-recommended user, and to effectively enhance precision and reliability of contents recommended to the user.

[0107] 304 ¨ writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0108] Specifically, the Flink stream processing engine can write the preference prediction result of the to-be-recommended user simultaneously in the cache component and the persistence component in the distributed storage layer, and write the triggered event in the message queue after obtaining response result returned from the cache component and the persistence component, so as to dispose the preference prediction result of the to-be-recommended user for use by downstream systems.

[0109] This embodiment of the present application provides a Flink stream processing engine method for real-time recommendation, the distributed stream engine Flink itself brings about precise message reliability and flexible capacities to expand and extract, thus makes it possible to guarantee message reliability and capacities to expand and extract of the system, and makes it possible to reduce resource consumption in normal times but to increase resources in large-scale promotional activities to respond to peak flow; the response duration by the use of an offline algorithm is usually of the hourly magnitude, and the operation time usually cannot be guaranteed, whereas the response time based on the use of the current engine is of the millisecond magnitude, whereby real-time recommendation and feature engineering are made possible, and response duration is thereby effectively reduced.

Date Recue/Date Received 2022-03-22

[0110] In one embodiment, prior to the aforementioned step 301 of performing short-term feature calculation on a log in a message queue according to a calculation model file, the method can further comprise:

[0111] obtaining a configuration file that contains a configuration parameter;
and

[0112] loading from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

[0113] The step of obtaining the configuration file can include:

[0114] synchronizing at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

[0115] Specifically, the configuration synchronizing module in the Flink stream processing engine can synchronize differently typed configuration files from the distributed configuration cluster, and send the configuration file to the algorithm calculating module located at the various distributed nodes, so that the algorithm calculating module loads the configuration file to the memories of the distributed nodes.

[0116] The step of loading from a model repository a model file to which the configuration parameter corresponds can include:

[0117] reading a preconfigured model identification parameter from the configuration parameter, and loading from the model repository a model file to which the model identification parameter corresponds, wherein the model identification parameter includes a namespace, a model name and a model version.

[0118] In a concrete example as shown in Fig. 4, the aforementioned process of loading the model file can include the following steps:

Date Recue/Date Received 2022-03-22

[0119] a) initializing the model repository client end at the application submission phase of the Flink stream processing engine;

[0120] b) loading the model file and the relevant script configuration file to the equipment in which the Flink stream processing engine resides;

[0121] c) employing distributed file cache by the equipment to submit the model and the relevant file to the various distributed nodes;

[0122] d) loading the model repository client end during task loading at the various nodes, obtaining the model required to be used according to the configuration and loading the model to the memory, and generating corresponding UDF; and

[0123] e) employing the model file in the memory during prediction to perform prediction.

[0124] In this embodiment, after the model file trained offline has been submitted to the model repository, it suffices to automatically load the model to the stream processing engine and operate the model through the model identification parameter of the model file required to be used in the configuration parameter, so that the difficulty in deploying the algorithm model and the difficulty in completing real-time response are avoided, one-stop automatic start and deployment of the model is realized, massive deployment operations are dispensed with, and error and time cost are reduced.

[0125] In one embodiment, the aforementioned step 301 of performing short-term feature calculation on a log in a message queue according to a calculation model file can include:

[0126] performing short-term feature calculation on the log according to the calculation model file through the various distributed nodes.

[0127] The aforementioned step 303 of performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user can include:

[0128] generating a prediction model according to the prediction model file through the various distributed nodes; and Date Recue/Date Received 2022-03-22

[0129] inputting the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

[0130] In the present application, the characteristic of distributed message response of Flink itself is utilized, whereby its parallel capability is maximally utilized to enhance the computing capability and to shorten prediction time at the same time.

[0131] In one embodiment, the distributed storage layer includes a cache component and a persistence component, and the aforementioned step 302 of reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue can include:

[0132] reading a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes; reading a long-telln feature and a short-term feature of the to-be-recommended user from the cache component, if reading from the local cache failed; and reading a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, if reading from the cache component failed, and asynchronously writing the read features in the cache component.

[0133] Specifically, different features in the NOSQL storage layer are read by each distributed node through the feature enquiring module (Query er) and based on the configuration parameter obtained in advance.

[0134] More specifically, with reference to the flowchart illustrating reading of three-level cache features in Fig. 5, the feature enquiring module firstly reads from a local primary cache, return directly ensues if hit, reads from a distributed secondary cache if not hit, return ensues from the secondary cache if hit, reads from a persistence layer if not hit, and employs a data synchronizer to asynchronously write back the result to the secondary cache for standby use next time.
Date Recue/Date Received 2022-03-22

[0135] The primary cache is the host memory that only retains related data, is relatively small in capacity, the expiration time thereof is relatively short (the expiration time should be configured according to the business), and utilizes the LRU policy. The secondary cache is a distributed cache that stores partial data, is relatively large in capacity, the expiration time thereof is relatively long (the expiration time should be configured according to the business), likewise utilizes the LRU policy, and random increase and decrease of the expiration time are performed within a certain range in order to avoid the avalanche effect.
The tertiary is persistence, retains total data, can be set to expire as one year or longer, and can be exchanged to an offline data warehouse if it should be retained for more longer time. The data synchronizer possesses a queue, and invoking of the cache method merely effects simple disposal in the queue; a thread can be separately initiated, and data in the queue is then consumed by the thread to enter distributed cache.

[0136] In this embodiment, through the guarantee by the three-level caching mechanism, loss of feature data can be greatly reduced, or the possibility of erring in the recommendation result caused by error in the process of loading the feature data can be reduced, so that the reading performance and reliability of the feature data can be ensured at the same time, whereby robustness of the entire system is enhanced.

[0137] In one embodiment, the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

[0138] In this embodiment, the stream processing engine is realized on the basis of the FLINK
and PMML technologies, which enable the algorithm personnel without Java language capability to process features in real time and to perform model prediction in real time merely by using streaming SQL and PMML model files in a general model repository, so as to solve the problems in which there are too many technical details by the use of JAVA
or PYTHON language for development, whereby development is slow, and it is difficult Date Recue/Date Received 2022-03-22 to timely respond to business requirements.

[0139] Fig. 6 is a view illustrating a cluster deployment framework provided by the present application, the cluster deployment framework is employed to realize the Flink stream processing engine method for real-time recommendation in the aforementioned embodiments, specifically, the cluster deployment framework can be deployed by the mode described below:

[0140] 1. preparation of a Zookeeper cluster: the specific Zookeeper cluster deployment mode has already been mature, and can be deployed or self-tuned according to the mode recommended in Fig 6. However, as should be noted, one set can be used for Zookeeper relevant to Hadoop, while another set should be used for Kafka.

[0141] 2. preparation of a Hadoop cluster: the specific Hadoop cluster deployment mode has already been mature, to which no repetition is redundantly made in this context, and it is suggested that a physical machine rather than a virtual machine be used, and it is also required to install HBase and Flink components on the cluster.

[0142] 3. preparation of a Redis cluster: the suggested deployment mode is three sentinels plus N*2 sets of servers, where N is the number of master nodes, the specific N
should be determined according to business scale, it is N*2 because one master is equipped with a one standby. The specific Redis cluster deployment method has already been mature, to which no repetition is redundantly made in this context, but it is suggested that it be deployed close to the calculation node, best within the single route to ensure lower network time delay.

[0143] 4. preparation of a Kafka cluster: the specific Kafka cluster deployment mode has already been mature, to which no repetition is redundantly made in this context, and reference can be made to the official website.

[0144] 5. preparation of a scheduler: Hadoop and Flink components are installed for submission of engine application.

[0145] 6. The Zookeeper, HDFS, YARN, Kafka and Redis clusters are started.

[0146] 7. A model repository is newly created on HDFS, and well trained models are released in Date Recue/Date Received 2022-03-22 the repository.

[0147] 8. Flink submission script is used and command line parameters are designated on the scheduler, wherein coordinates of the models are indispensable parameters, while the remaining is optional.

[0148] In one embodiment, there is provided a Flink stream processing engine device for real-time recommendation, which device can comprise:

[0149] an algorithm calculating module, for performing short-term feature calculation on a log in a message queue according to a calculation model file;

[0150] a data pushing module, for storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance; and

[0151] a feature enquiring module, for reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0152] the algorithm calculating module is further employed for performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0153] the data pushing module is further employed for writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0154] In one embodiment, the device further comprises:

[0155] a configuration synchronizing module, for obtaining a configuration file that contains a configuration parameter; and

[0156] a model loading module, for loading from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

Date Recue/Date Received 2022-03-22

[0157] In one embodiment, the configuration synchronizing module is specifically employed for:

[0158] synchronizing at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

[0159] In one embodiment, the model loading module is specifically employed for:

[0160] reading a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and

[0161] loading from the model repository a model file to which the model identification parameter corresponds.

[0162] In one embodiment, the algorithm calculating module is specifically employed for:

[0163] performing short-term feature calculation on the log according to the calculation model file through the various distributed nodes;

[0164] the algorithm calculating module is specifically further employed for:

[0165] generating a prediction model according to the prediction model file through the various distributed nodes; and

[0166] inputting the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

[0167] In one embodiment, the distributed storage layer includes a cache component and a persistence component, and the feature enquiring module is specifically employed for:

[0168] reading a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;

[0169] reading a long-term feature and a short-term feature of the to-be-recommended user from the cache component, if reading from the local cache failed; and

[0170] reading a long-term feature and a short-term feature of the to-be-recommended user from Date Recue/Date Received 2022-03-22 the persistence component, if reading from the cache component failed, and asynchronously writing the read features in the cache component.

[0171] In one embodiment, the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

[0172] Specific definitions relevant to the user recommending device based on stream processing may be inferred from the aforementioned definitions to the Flink stream processing engine method for real-time recommendation, while no repetition is made in this context.
The various modules in the aforementioned the user recommending device based on stream processing can be wholly or partly realized via software, hardware, and a combination of software with hardware. The various modules can be embedded in the form of hardware in a processor in a computer equipment or independent of any computer equipment, and can also be stored in the form of software in a memory in a computer equipment, so as to facilitate the processor to invoke and perform operations corresponding to the aforementioned various modules.

[0173] In one embodiment, a computer equipment is provided, the computer equipment can be a server, and its internal structure can be as shown in Fig. 7. The computer equipment comprises a processor, a memory, and a network interface connected to each other via a system bus. The processor of the computer equipment is employed to provide computing and controlling capabilities. The memory of the computer equipment includes a nonvolatile storage medium, and an internal memory. The nonvolatile storage medium stores therein an operating system, and a computer program. The internal memory provides environment for the running of the operating system and the computer program in the nonvolatile storage medium. The network interface of the computer equipment is employed to connect to other equipment via network for communication. The computer program realizes a Flink stream processing engine method for real-time recommendation when it is executed by a processor.
Date Recue/Date Received 2022-03-22

[0174] As understandable to persons skilled in the art, the structure illustrated in Fig. 7 is merely a block diagram of partial structure relevant to the solution of the present application, and does not constitute any restriction to the computer equipment on which the solution of the present application is applied, as the specific computer equipment may comprise component parts that are more than or less than those illustrated in Fig. 7, or may combine certain component parts, or may have different layout of component parts.

[0175] In one embodiment, there is provided a computer equipment that comprises a memory, a processor and a computer program stored on the memory and operable on the processor, and the following steps are realized when the processor executes the computer program:

[0176] performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;

[0177] reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0178] performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0179] writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0180] In one embodiment, there is provided a computer-readable storage medium storing thereon a computer program, and the following steps are realized when the computer program is executed by a processor:

[0181] performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term Date Recue/Date Received 2022-03-22 features obtained in advance;

[0182] reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer, when an event is received from the message queue;

[0183] performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and

[0184] writing the preference prediction result of the to-be-recommended user in the distributed storage layer.

[0185] As comprehensible to persons ordinarily skilled in the art, the entire or partial flows in the methods according to the aforementioned embodiments can be completed via a computer program instructing relevant hardware, the computer program can be stored in a nonvolatile computer-readable storage medium, and the computer program can include the flows as embodied in the aforementioned various methods when executed. Any reference to the memory, storage, database or other media used in the various embodiments provided by the present application can all include nonvolatile and/or volatile memory/memories. The nonvolatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM) or a flash memory. The volatile memory can include a random access memory (RAM) or an external cache memory. To serve as explanation rather than restriction, the RAM is obtainable in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM
(SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM
(RDRAM), etc.

[0186] Technical features of the aforementioned embodiments are randomly combinable, while all possible combinations of the technical features in the aforementioned embodiments Date Recue/Date Received 2022-03-22 are not exhausted for the sake of brevity, but all these should be considered to fall within the scope recorded in the Description as long as such combinations of the technical features are not mutually contradictory.

[0187] The foregoing embodiments are merely directed to several modes of execution of the present application, and their descriptions are relatively specific and detailed, but they should not be hence misunderstood as restrictions to the inventive patent scope. As should be pointed out, persons with ordinary skill in the art may further make various modifications and improvements without departing from the conception of the present application, and all these should pertain to the protection scope of the present application.
Accordingly, the patent protection scope of the present application shall be based on the attached Claims.

Date Recue/Date Received 2022-03-22

Claims

Claims:

1. A Flink stream processing engine device for real-time recommendation, comprising:
an algorithm calculating module configured to perform short-term feature calculation on a log in a message queue according to a calculation model file;
a data pushing module configured to store short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;
a feature enquiring module configured to, on receipt of an event from the message queue, read a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer;
the algorithm calculating module further configured to perform real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and the data pushing module further configured to write the preference prediction result of the to-be-recommended user in the distributed storage layer.

2. The device of claim 1 further comprising a message queue connecting module configured to obtain data from the message queue.

3. The device of claim 2 wherein the message queue stores log data.

4. The device of claim 3 wherein the log data includes behavior data generated by user behavior operations within a preset time period.

5. The device of claim 4 wherein the behavior operations include at least one of instance browsing, clicking, listing as favorite, sharing a commodity object operated by the user.

Date Recue/Date Received 2023-12-05

6. The device of any one of claims 4 or 5 wherein the preset time period is set according to practical requirements.

7. The device of claim 6 wherein the distributed storage layer is an NOSQL
storage layer which includes a cache component and a persistence component.

8. The device of claim 7 wherein the cache component is a Redis component which has a distributed, high-reading, and high-writing, small storage capacity, and stores data within a period of time.

9. The device of any one of claims 7 or 8 wherein the persistence component is an Hbase component which has a distributed, low-reading, and high-writing, large storage capacity, and column-extensible properties, and stores data beyond a period of time.

10. The device of any one of claims 7 to 9 wherein the cache component and the persistence component can both be used to store the long-term features and the short-term features.

11. The device of any one of claims 1 to 10 wherein the long-term features represent long-term preferences of the user with respect to a commodity.

12. The device of claim 11 wherein the long-term preferences of the user include at least one of user basic features, user long-term behavior features and other features.

13. The device of claim 12 wherein user basic features include at least one of age, gender, profession, place of residence, and preference of the user.

14. The device of any one of claims 11 or 12 wherein the user long-term behaviour features include shopping behaviour statistic values at an e-commerce platform within a period of time.

15. The device of claim 10 wherein the short-term features represent short-term preferences of the user with respect to a commodity.
Date Recue/Date Received 2023-12-05

16. The device of claim 15 wherein the short-term preferences are obtained from behavior operation data generated by the user within the preset time period.

17. The device of any one of claims 1 to 16 further comprising a first machine learning algorithm to train primary data of a sample user to generate a short-term model and to obtain the calculation model file.

18. The device of claim 17 wherein the first machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

19. The device of any one of claims 1 to 18 wherein the to-be-recommended user is determined based on a preset user identification.

20. The device of any one of claims 1 to 19 further comprising a second machine learning algorithm to generate the prediction model file.

21. The device of claim 20 wherein the second machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

22. The device of any one of claims 1 to 21, further comprising:
a configuration synchronization module configured to obtain a configuration file that contains a configuration parameter; and a model loading module configured to load from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink steam processing engine, wherein the model file includes the calculation model file and the prediction model file.

23. The device according to Claim 22, wherein the configuration synchronizing module is further configured to:
synchronize at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

Date Recue/Date Received 2023-12-05

24. The device according to Claim 23, wherein the model loading module is further configured to:
read a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and load from the model repository a model file to which the model identification parameter corresponds.

25. The device of claim 24 wherein the model loading module is further configured to:
initialize a model repository client end at an application submission phase of the Flink stream processing engine;
load the model file and a relevant script configuration file to equipment in which the Flink stream processing engine resides;
employ distributed file cache by the equipment to submit the model file and the relevant script configuration file to the various distributed nodes;
load the model repository client end during a task loading at the various nodes, obtaining the model from the model file required to be used according to the configuration file and loading the model to the memory, and generating corresponding UDF; and employ the model file in the memory during prediction to perform prediction.

26. The device of any one of claims 22 to 25 wherein the model file is automatically loaded and deployed from the repository.

27. The device of claim 22, wherein the algorithm calculating module is further configured to:
perform short-term feature calculation on the log according to the calculation model file through the various distributed nodes;

Date Recue/Date Received 2023-12-05 generate a prediction model according to the prediction model file through the various distributed nodes; and input the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

28. The device of claim 7, wherein the distributed storage layer includes a cache component and the persistence component, and the feature enquiring module is further configured to:
read a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;
if reading from the local cache failed, read a long-term feature and a short-term feature of the to-be-recommended user from the cache component; and if reading from the cache component failed, read a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, and asynchronously writing the read features in the cache component.

29. The device of claim 28 wherein the cache component is a distributed secondary cache component.

30. The device of claim 29 wherein the secondary cache component stores partial data, has a large capacity, a long expiration time, and utilizes an LRU policy.

31. The device of claim 29 wherein a data synchronizer is employed to asynchronously write a result to the secondary cache component for standby use in the future.

32. The device of claim 28 wherein the local cache is a host memory that only retains related data, has a small capacity, a short expiration time, and utilizes an LRU
policy.

33. The device of claim 28 wherein the persistence component retains total data for at least a year.

Date Recue/Date Received 2023-12-05

34. The device of claim 28 wherein the persistence component can be an offline data warehouse.

35. The device of any one of claims 1 to 34, characterized in that the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

36. The device of any one of claims 1 to 35 further comprising utilizing a cluster deployment framework including:
preparing a Zookeeper cluster;
preparing a Hadoop cluster;
preparing a Redis cluster, wherein a deployment mode is three sentinels plus N*2 sets of servers, wherein N is a number of master nodes;
preparing a Kafka cluster;
preparing a scheduler, using Hadoop and Flink components;
starting the Zookeeper, Hadoop, Kafka and Redis clusters; and creating a model repository on HDFS, wherein trained models are released in the repository.

37. A Flink stream processing engine device for real-time recommendation, comprising:
an algorithm calculating module configured to perfolin short-term feature calculation on a log in a message queue according to a calculation model file;
a data pushing module configured to store short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;

Date Recue/Date Received 2023-12-05 a feature enquiring module configured to, on receipt of an event from the message queue, read a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer;
the algorithm calculating module further configured to perform real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and the data pushing module further configured to write the preference prediction result of the to-be-recommended user in the distributed storage layer.

38. The device of claim 37 further comprising a message queue connecting module configured to obtain data from the message queue.

39. The device of claim 38 wherein the message queue stores log data.

40. The device of claim 39 wherein the log data includes behavior data generated by user behavior operations within a preset time period.

41. The device of claim 40 wherein the behavior operations include at least one of instance browsing, clicking, listing as favorite, sharing a commodity object operated by the user.

42. The device of any one of claims 40 or 41 wherein the preset time period is set according to practical requirements.

43. The device of claim 42 wherein the distributed storage layer is an NOSQL
storage layer which includes a cache component and a persistence component.

44. The device of claim 43 wherein the cache component is a Redis component which has a distributed, high-reading, and high-writing, small storage capacity, and stores data within a period of time.
Date Recue/Date Received 2023-12-05

45. The device of any one of claims 43 or 44 wherein the persistence component is an Hbase component which has a distributed, low-reading, and high-writing, large storage capacity, and column-extensible properties, and stores data beyond a period of time.

46. The device of any one of claims 43 to 45 wherein the cache component and the persistence component can both be used to store the long-telm features and the short-term features.

47. The device of any one of claims 37 to 46 wherein the long-term features represent long-term preferences of the user with respect to a commodity.

48. The device of claim 47 wherein the long-term preferences of the user include at least one of user basic features, user long-term behavior features and other features.

49. The device of claim 48 wherein user basic features include at least one of age, gender, profession, place of residence, and preference of the user.

50. The device of any one of claims 47 or 48 wherein the user long-term behaviour features include shopping behaviour statistic values at an e-commerce platform within a period of time.

51. The device of claim 46 wherein the short-term features represent short-term preferences of a user with respect to a commodity.

52. The device of claim 51 wherein the short-term preferences are obtained from behavior operation data generated by the user within the preset time period.

53. The device of any one of claims 37 to 52 further comprising a first machine learning algorithm to train primary data of a sample user to generate a short-term model and to obtain the calculation model file.

54. The device of claim 53 wherein the first machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

Date Recue/Date Received 2023-12-05

55. The device of any one of claims 37 to 54 wherein the to-be-recommended user is determined based on a preset user identification.

56. The device of any one of claims 37 to 55 further comprising a second machine learning algorithm to generate the prediction model file.

57. The device of claim 56 wherein the second machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

58. The device of any one of claims 37 to 57, further comprising:
a configuration synchronization module configured to obtain a configuration file that contains a configuration parameter; and a model loading module configured to load from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

59. The device according to Claim 58, wherein the configuration synchronizing module is further configured to:
synchronize at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

60. The device according to Claim 59, wherein the model loading module is further configured to:
read a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and load from the model repository a model file to which the model identification parameter corresponds.

Date Recue/Date Received 2023-12-05

61. The device of claim 60 wherein the model loading module is further configured to:
initialize a model repository client end at an application submission phase of the Flink stream processing engine;
load the model file and a relevant script configuration file to equipment in which the Flink stream processing engine resides;
employ distributed file cache by the equipment to submit the model file and the relevant script configuration file to the various distributed nodes;
load the model repository client end during a task loading at the various nodes, obtaining the model from the model file required to be used according to the configuration file and loading the model to the memory, and generating corresponding UDF; and employ the model file in the memory during prediction to perform prediction.

62. The device of any one of claims 58 to 61 wherein the model file is automatically loaded and deployed from the repository.

63. The device of claim 58, wherein the algorithm calculating module is further configured to:
perform short-term feature calculation on the log according to the calculation model file through the various distributed nodes;
generate a prediction model according to the prediction model file through the various distributed nodes; and input the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

64. The device of claim 43, wherein the distributed storage layer includes a cache component and the persistence component, and the feature enquiring module is further configured to:

Date Recue/Date Received 2023-12-05 read a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;
if reading from the local cache failed, read a long-term feature and a short-term feature of the to-be-recommended user from the cache component; and if reading from the cache component failed, read a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, and asynchronously writing the read features in the cache component.

65. The device of claim 64 wherein the cache component is a distributed secondary cache component.

66. The device of claim 65 wherein the secondary cache component stores partial data, has a large capacity, a long expiration time, and utilizes an LRU policy.

67. The device of claim 65 wherein a data synchronizer is employed to asynchronously write a result to the secondary cache component for standby use in the future.

68. The device of claim 64 wherein the local cache is a host memory that only retains related data, has a small capacity, a short expiration time, and utilizes an LRU
policy.

69. The device of claim 64 wherein the persistence component retains total data for at least a year.

70. The device of claim 64 wherein the persistence component can be an offline data warehouse.

71. The device of any one of claims 37 to 70, characterized in that the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

72. The device of any one of claims 37 to 71 further comprising utilizing a cluster deployment framework including:

Date Recue/Date Received 2023-12-05 preparing a Zookeeper cluster;
preparing a Hadoop cluster;
preparing a Redis cluster, wherein a deployment mode is three sentinels plus N*2 sets of servers, wherein N is a number of master nodes;
preparing a Kafka cluster;
preparing a scheduler, using Hadoop and Flink components;
starting the Zookeeper, Hadoop, Kafka and Redis clusters; and creating a model repository on HDFS, wherein trained models are released in the repository.

73. A Flink stream processing engine method for real-time recommendation, characterized in comprising:
performing short-term feature calculation on a log in a message queue according to a calculation model file, and storing short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;
on receipt of an event from the message queue, reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer;
performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and writing the preference prediction result of the to-be-recommended user in the distributed storage layer.
Date Recue/Date Received 2023-12-05

74. The method of claim 73 wherein the message queue is a mode of implementation of a middleware.

75. The method of claim 74 wherein the message queue stores log data.

76. The method of claim 75 wherein the log data includes behavior data generated by user behavior operations within a preset time period.

77. The method of claim 76 wherein the behavior operations include at least one of instance browsing, clicking, listing as favorite, sharing a commodity object operated by the user.

78. The method of any one of claims 76 or 77 wherein the preset time period is set according to practical requirements.

79. The method of claim 78 wherein the distributed storage layer is an NOSQL
storage layer which includes a cache component and a persistence component.

80. The method of claim 79 wherein the cache component is a Redis component which has a distributed, high-reading, and high-writing, small storage capacity, and stores data within a period of time.

81. The method of any one of claims 79 or 80 wherein the persistence component is an Hbase component which has a distributed, low-reading, and high-writing, large storage capacity, and column-extensible properties, and stores data beyond a period of time.

82. The method of any one of claims 79 to 81 wherein the cache component and the persistence component can both be used to store the long-tenn features and the short-term features.

83. The method of any one of claims 73 to 82 wherein the long-term features represent long-term preferences of the user with respect to a commodity.

84. The method of claim 83 wherein the long-term preferences of the user include at least one of user basic features, user long-telln behavior features and other features.

Date Recue/Date Received 2023-12-05

85. The method of claim 84 wherein user basic features include at least one of age, gender, profession, place of residence, and preference of the user.

86. The method of any one of claims 83 or 84 wherein the user long-term behaviour features include shopping behaviour statistic values at an e-commerce platform within a period of time.

87. The method of claim 82 wherein the short-term features represent short-term preferences of the user with respect to a commodity.

88. The method of claim 87 wherein the short-term preferences are obtained from behavior operation data generated by the user within the preset time period.

89. The method of any one of claims 73 to 88 further comprising employing a first machine learning algorithm to train primary data of a sample user to generate a short-term model and to obtain the calculation model file.

90. The method of claim 89 wherein the first machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

91. The method of any one of claims 73 to 90 wherein the to-be-recommended user is determined based on a preset user identification.

92. The method of any one of claims 73 to 91 further comprising employing a second machine learning algorithm to generate the prediction model file.

93. The method of claim 92 wherein the second machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

94. The method of any one of claims 73 to 93, further comprising: prior to the step of performing short-term feature calculation on a log in a message queue according to a calculation model file:
obtaining a configuration file that contains a configuration parameter; and Date Recue/Date Received 2023-12-05 loading from a model repository a model file well trained in advance to which the configuration parameter corresponds, and sending the model file to various distributed nodes of the Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

95. The method according to Claim 94, characterized in that the step of obtaining a configuration file includes:
synchronizing at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

96. The method according to Claim 95, characterized in that the step of loading from a model repository a model file to which the configuration parameter corresponds includes:
reading a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and loading from the model repository a model file to which the model identification parameter corresponds.

97. The method of claim 96 wherein loading from a model repository a model file comprises:
initializing a model repository client end at an application submission phase of the Flink stream processing engine;
loading the model file and a relevant script configuration file to equipment in which the Flink stream processing engine resides;
employing distributed file cache by the equipment to submit the model file and the relevant script configuration file to the various distributed nodes;

Date Recue/Date Received 2023-12-05 loading the model repository client end during a task loading at the various nodes, obtaining the model from the model file required to be used according to the configuration file and loading the model to the memory, and generating corresponding UDF; and employing the model file in the memory during prediction to perform prediction.

98. The method of any one of claims 94 to 97 wherein the model file is automatically loaded and deployed from the repository.

99. The method of claim 94, characterized in that the step of performing short-term feature calculation on a log in a message queue according to a calculation model file includes:
performing short-term feature calculation on the log according to the calculation model file through the various distributed nodes; and the step of performing real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user includes:
generating a prediction model according to the prediction model file through the various distributed nodes; and inputting the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

100. The method of claim 79, characterized in that the distributed storage layer includes a cache component and the persistence component, and the step of on receipt of an event from the message queue, reading a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer:
reading a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;

Date Recue/Date Received 2023-12-05 if reading from the local cache failed, reading a long-term feature and a short-term feature of the to-be-recommended user from the cache component and if reading from the cache component failed, reading a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, and asynchronously writing the read features in the cache component.

101. The method of claim 100 wherein the cache component is a distributed secondary cache component.

102. The method of claim 101 wherein the secondary cache component stores partial data, has a large capacity, a long expiration time, and utilizes an LRU policy.

103. The method of claim 101 wherein a data synchronizer is employed to asynchronously write a result to the secondary cache component for standby use in the future.

104. The method of claim 100 wherein the local cache is a host memory that only retains related data, has a small capacity, a short expiration time, and utilizes an LRU
policy.

105. The method of claim 100 wherein the persistence component retains total data for at least a year.

106. The method of claim 100 wherein the persistence component can be an offline data warehouse.

107. The method of any one of claims 73 to 106, characterized in that the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

108. The method of any one of claims 73 to 107 further comprising utilizing a cluster deployment framework including:
preparing a Zookeeper cluster;
preparing a Hadoop cluster;
Date Recue/Date Received 2023-12-05 preparing a Redis cluster, wherein a deployment mode is three sentinels plus N*2 sets of servers, wherein N is a number of master nodes;
preparing a Kafka cluster;
preparing a scheduler, using Hadoop and Flink components;
starting the Zookeeper, Hadoop, Kafka and Redis clusters; and creating a model repository on HDFS, wherein trained models are released in the repository.

109. A Flink steam processing computer equipment comprising:
a computer readable physical memory;
a processor communicatively coupled to the physical memory, a computer program stored on the physical memory and operable on the processor, wherein the processor executes the computer program configured to:
perform short-term feature calculation on a log in a message queue according to a calculation model file, and store short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;
on receipt of an event from the message queue, read a long-term feature and a short-term feature of a to-be-recommended user from the distributed storage layer;
perform real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and write the preference prediction result of the to-be-recommended user in the distributed storage layer.

Date Recue/Date Received 2023-12-05

110. The equipment of claim 109 wherein the message queue is a mode of implementation of a middleware.

111. The equipment of claim 110 wherein the message queue stores log data.

112. The equipment of claim 111 wherein the log data includes behavior data generated by user behavior operations within a preset time period.

113. The equipment of claim 112 wherein the behavior operations include at least one of instance browsing, clicking, listing as favorite, sharing a commodity object operated by the user.

114. The equipment of any one of claims 112 or 113 wherein the preset time period is set according to practical requirements.

115. The equipment of claim 114 wherein the distributed storage layer is an NOSQL storage layer which includes a cache component and a persistence component.

116. The equipment of claim 115 wherein the cache component is a Redis component which has a distributed, high-reading, and high-writing, small storage capacity, and stores data within a period of time.

117. The equipment of any one of claims 115 or 116 wherein the persistence component is an Hbase component which has a distributed, low-reading, and high-writing, large storage capacity, and column-extensible properties, and stores data beyond a period of time.

118. The equipment of any one of claims 115 to 117 wherein the cache component and the persistence component can both be used to store the long-term features and the short-term features.

119. The equipment of any one of claims 109 to 118 wherein the long-term features represent long-term preferences of the user with respect to a commodity.

120. The equipment of claim 119 wherein the long-term preferences of the user include at least one of user basic features, user long-term behavior features and other features.

Date Recue/Date Received 2023-12-05

121. The equipment of claim 120 wherein user basic features include at least one of age, gender, profession, place of residence, and preference of the user.

122. The equipment of any one of claims 119 or 120 wherein the user long-term behaviour features include shopping behaviour statistic values at an e-commerce platform within a period of time.

123. The equipment of claim 118 wherein the short-term features represent short-term preferences of the user with respect to a commodity.

124. The equipment of claim 123 wherein the short-term preferences are obtained from behavior operation data generated by the user within the preset time period.

125. The equipment of any one of claims 109 to 124 further configured to employ a first machine learning algorithm to train primary data of a sample user to generate a short-term model and to obtain the calculation model file.

126. The equipment of claim 125 wherein the first machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

127. The equipment of any one of claims 109 to 126 wherein the to-be-recommended user is determined based on a preset user identification.

128. The equipment of any one of claims 109 to 127 further configured to employ a second machine learning algorithm to generate the prediction model file.

129. The equipment of claim 128 wherein the second machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

130. The equipment of any one of claims 109 to 129, further configured to:
prior to the step of performing short-term feature calculation on a log in a message queue according to a calculation model file:
obtain a configuration file that contains a configuration parameter; and Date Recue/Date Received 2023-12-05 load from a model repository a model file well trained in advance to which the configuration parameter corresponds, and send the model file to various distributed nodes of a Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

131. The equipment according to Claim 130, further configured to:
synchronize at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

132. The equipment according to Claim 131, further configured to:
read a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and load from the model repository a model file to which the model identification parameter corresponds.

133. The equipment of claim 132 further configured to:
initialize a model repository client end at an application submission phase of the Flink stream processing engine;
load the model file and a relevant script configuration file to equipment in which the Flink stream processing engine resides;
employ distributed file cache by the equipment to submit the model file and the relevant script configuration file to the various distributed nodes;
load the model repository client end during a task loading at the various nodes, obtaining the model from the model file required to be used according to the configuration file and loading the model to the memory, and generating corresponding UDF; and Date Recue/Date Received 2023-12-05 employ the model file in the memory during prediction to perform prediction.

134. The equipment of any one of claims 130 to 133 wherein the model file is automatically loaded and deployed from the repository.

135. The equipment of claim 130, further configured to:
perform short-term feature calculation on the log according to the calculation model file through the various distributed nodes;
generate a prediction model according to the prediction model file through the various distributed nodes; and input the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.

136. The equipment of claim 115, further configured to:
read a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;
if reading from the local cache failed, read a long-term feature and a short-term feature of the to-be-recommended user from the cache component; and if reading from the cache component failed, read a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, and asynchronously writing the read features in the cache component.

137. The equipment of claim 136 wherein the cache component is a distributed secondary cache component.

138. The equipment of claim 137 wherein the secondary cache component stores partial data, has a large capacity, a long expiration time, and utilizes an LRU policy.
Date Recue/Date Received 2023-12-05

139. The equipment of claim 137 wherein a data synchronizer is employed to asynchronously write a result to the secondary cache component for standby use in the future.

140. The equipment of claim 136 wherein the local cache is a host memory that only retains related data, has a small capacity, a short expiration time, and utilizes an LRU policy.

141. The equipment of claim 136 wherein the persistence component retains total data for at least a year.

142. The equipment of claim 136 wherein the persistence component can be an offline data warehouse.

143. The equipment of any one of claims 109 to 142, characterized in that the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

144. The equipment of any one of claims 109 to 143 further configured to utilize a cluster deployment framework including:
preparing a Zookeeper cluster;
preparing a Hadoop cluster;
preparing a Redis cluster, wherein a deployment mode is three sentinels plus N*2 sets of servers, wherein N is a number of master nodes;
preparing a Kafka cluster;
preparing a scheduler, using Hadoop and Flink components;
starting the Zookeeper, Hadoop, Kafka and Redis clusters; and creating a model repository on HDFS, wherein trained models are released in the repository.

Date Recue/Date Received 2023-12-05

145.A computer readable physical memory having stored thereon a computer program executed by a computer configured to:
perform short-term feature calculation on a log in a message queue according to a calculation model file, and store short-term features obtained by the calculation in a distributed storage layer, wherein the distributed storage layer stores therein long-term features obtained in advance;
on receipt of an event from the message queue, read a long-term feature and a short-tenn feature of a to-be-recommended user from the distributed storage layer;
perform real-time prediction according to a prediction model file, the long-term feature and the short-term feature of the to-be-recommended user, to obtain a preference prediction result of the to-be-recommended user; and write the preference prediction result of the to-be-recommended user in the distributed storage layer.

146. The memory of claim 145 wherein the message queue is a mode of implementation of a middleware.

147. The memory of claim 146 wherein the message queue stores log data.

148. The memory of claim 147 wherein the log data includes behavior data generated by user behavior operations within a preset time period.

149. The memory of claim 148 wherein the behavior operations include at least one of instance browsing, clicking, listing as favorite, sharing a commodity object operated by the user.

150. The memory of any one of claims 148 or 149 wherein the preset time period is set according to practical requirements.

151. The memory of claim 150 wherein the distributed storage layer is an NOSQL
storage layer which includes a cache component and a persistence component.

Date Recue/Date Received 2023-12-05

152. The memory of claim 151 wherein the cache component is a Redis component which has a distributed, high-reading, and high-writing, small storage capacity, and stores data within a period of time.

153. The memory of any one of claims 151 or 152 wherein the persistence component is an Hbase component which has a distributed, low-reading, and high-writing, large storage capacity, and column-extensible properties, and stores data beyond a period of time.

154. The memory of any one of claims 151 to 153 wherein the cache component and the persistence component can both be used to store the long-term features and the short-term features.

155. The memory of any one of claims 145 to 154 wherein the long-term features represent long-term preferences of the user with respect to a commodity.

156. The memory of claim 155 wherein the long-term preferences of the user include at least one of user basic features, user long-term behavior features and other features.

157. The memory of claim 156 wherein user basic features include at least one of age, gender, profession, place of residence, and preference of the user.

158. The memory of any one of claims 155 or 156 wherein the user long-term behaviour features include shopping behaviour statistic values at an e-commerce platform within a period of time.

159. The memory of claim 154 wherein the short-term features represent short-term preferences of the user with respect to a commodity.

160. The memory of claim 159 wherein the short-term preferences are obtained from behavior operation data generated by the user within the preset time period.

161. The memory of any one of claims 145 to 160 further configured to employ a first machine learning algorithm to train primary data of a sample user to generate a short-term model and to obtain the calculation model file.

Date Recue/Date Received 2023-12-05

162. The memory of claim 161 wherein the first machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

163. The memory of any one of claims 145 to 162 wherein the to-be-recommended user is determined based on a preset user identification.

164. The memory of any one of claims 145 to 163 further configured to employ a second machine learning algorithm to generate the prediction model file.

165. The memory of claim 164 wherein the second machine learning algorithm is embodied as one of an iterative decision tree, a logic regression, and a support vector machine.

166. The memory of any one of claims 145 to 165, further configured to: prior to the step of performing short-term feature calculation on a log in a message queue according to a calculation model file:
obtain a configuration file that contains a configuration parameter; and load from a model repository a model file well trained in advance to which the configuration parameter corresponds, and send the model file to various distributed nodes of a Flink stream processing engine, wherein the model file includes the calculation model file and the prediction model file.

167. The memory according to Claim 166, further configured to:
synchronize at least one configuration file from a distributed configuration cluster, and loading the at least one configuration file into a memory.

168. The memory according to Claim 166, further configured to:
read a preconfigured model identification parameter from the configuration parameter, wherein the model identification parameter includes a namespace, a model name and a model version; and Date Recue/Date Received 2023-12-05 load from the model repository a model file to which the model identification parameter corresponds.

169. The memory of any one of claims 166 to 168 further configured to:
initialize a model repository client end at an application submission phase of the Flink stream processing engine;
load the model file and a relevant script configuration file to memory in which the Flink stream processing engine resides;
employ distributed file cache by the memory to submit the model file and the relevant script configuration file to the various distributed nodes;
load the model repository client end during a task loading at the various nodes, obtaining the model from the model file required to be used according to the configuration file and loading the model to the memory, and generating corresponding UDF; and employ the model file in the memory during prediction to perform prediction.

170. The memory of any one of claims 166 to 169 wherein the model file is automatically loaded and deployed from the repository.

171. The memory of claim 166, further configured to:
perform short-term feature calculation on the log according to the calculation model file through the various distributed nodes;
generate a prediction model according to the prediction model file through the various distributed nodes; and input the long-term feature and the short-term feature of the to-be-recommended user to the prediction model for real-time prediction, to obtain the preference prediction result of the to-be-recommended user.
Date Recue/Date Received 2023-12-05

172. The memory of claim 151, further configured to:
read a long-term feature and a short-term feature of the to-be-recommended user from a local cache through the various distributed nodes;
if reading from the local cache failed, read a long-term feature and a short-term feature of the to-be-recommended user from the cache component; and if reading from the cache component failed, read a long-term feature and a short-term feature of the to-be-recommended user from the persistence component, and asynchronously writing the read features in the cache component.

173. The memory of claim 172 wherein the cache component is a distributed secondary cache component.

174. The memory of claim 173 wherein the secondary cache component stores partial data, has a large capacity, a long expiration time, and utilizes an LRU policy.

175. The memory of claim 173 wherein a data synchronizer is employed to asynchronously write a result to the secondary cache component for standby use in the future.

176. The memory of claim 172 wherein the local cache is a host memory that only retains related data, has a small capacity, a short expiration time, and utilizes an LRU
policy.

177. The memory of claim 172 wherein the persistence component retains total data for at least a year.

178. The memory of claim 172 wherein the persistence component can be an offline data warehouse.

179. The memory of any one of claims 145 to 178, characterized in that the calculation model file is an SQL model file, and the prediction model file is a PMML model file.

Date Recue/Date Received 2023-12-05

180. The memory of any one of claims 145 to 179 further configured to utilize a cluster deployment framework including:
preparing a Zookeeper cluster;
preparing a Hadoop cluster;
preparing a Redis cluster, wherein a deployment mode is three sentinels plus N*2 sets of servers, wherein N is a number of master nodes;
preparing a Kafka cluster;
preparing a scheduler, using Hadoop and Flink components;
starting the Zookeeper, Hadoop, Kafka and Redis clusters; and creating a model repository on HDFS, wherein trained models are released in the repository.

Date Recue/Date Received 2023-12-05