Design And Implementation Of Commercial Cash Platform Based On Elasticsearch

With the development of Internet and the popularity of 5G,short video has become one of the fastest growing segments of DAU,and the competition among major short video platforms is becoming increasingly fierce.With the peak of short video demographic dividend,and the division of traffic has finished,there is a competition around "reserve" and "commercial cash efficiency".Based on the influx of traffic and the urgent demand for cash flow,commercial cash flow system emerges as the times require.It is committed to building a talent ecological marketing platform to match customers’ marketing needs and talent cash flow demands.Through live or commercial short video,it helps users achieve the goals of live delivery,App download,brand marketing,form collection,etc.In the initial construction stage of the commercial realization platform,the technology selection is carried out firstly,the elasticsearch engine is used to store and retrieve the talent data and hot list in near real time;Considering the high concurrency in the live scene,redis is selected to reduce the concurrency in a short time;In order to facilitate asynchronous communication between messages and log processing,Kafka is used to realize complex log processing and asynchronous call notification between services.Based on the advantages of the platform,the complete function points of the system are obtained.According to the classification of the function points,the function division of the module is given through the use case diagram.Then the non functional requirements are required from the aspects of ease of use,security,reliability and robustness.In order to meet the needs of different users for system functions,the system is divided into six modules: task management,talent square,data display,talent intelligent recommendation,user information management and basic services.The system functions are introduced in detail from different perspectives through flow chart and function module diagram.Compared with the traditional recommendation algorithm,the intelligent recommendation module adopts the collaborative filtering model based on talents for recall and the feature-based model for fine arrangement.However,because the inner product function limits the expressiveness of the user article interaction,it also the conv MF(convolution matching function)model are adopted,it uses convolution neural network(CNN)to fully excavate the interaction characteristics between users and items,and then uses more refined features and complex wide&deep class model for fine arrangement in the sorting stage.Through this design,it helps customers find the talents in related fields more accurately,and achieves the goals of advertisers’ promotion,talents’ profit and platform development;Through multi label orientation,talent traffic boosts the use of fan headline algorithm to help users get high-value exposure and fans,to meet the urgent promotion needs of talent;In the detailed design stage,the attributes of the class and the relationship between the classes are described by the class diagram,the interaction between different services is defined by the sequence diagram,and the execution process of the data flow is described by the flow graph.In the test phase,the environment,tools and test content are given,and the test results are shown through the screenshot.Through the optimization and upgrading of the platform functions,matching the marketing needs of customers and the realization demands of talents,coupled with the active community atmosphere of the platform,the strong commercial transformation power of the platform has been stimulated.At present,the project designed in this paper has been put online,and has become a bridge between the company’s promotion needs and the realization needs of advertisers.After going online,the DAU has increased by nearly 50%,which made outstanding contributions to the development of commercialization.