Design And Implementation Of Automatic Gift Recommendation System

With the development of the Internet platform,many e-commerce portal platforms are beginning to face the problem of processing large amounts of operational data.Not only to solve the big data high concurrency platform performance problems,but also how to use big data to optimize business logic,to achieve personalized data recommendations for thousands of people.Traditional big data optimization solutions(such as: using a caching framework,optimizing data structures,changing database types,optimizing business logic,etc.)can alleviate performance problems under high concurrency to a certain extent,but need to make a significant impact on data structures or business systems.The adjustment can be achieved.Often,the task is heavy and the construction period is long.There are technical risks to the business platform and data.From the perspective of transformation,it is often at the expense of user experience in exchange for the stability of the platform.This paper takes the research and implementation of the big data project of the intelligent gift recommendation platform as the main line,and makes in-depth research and elaboration on the following three core links.(1)Research and improve the traditional web crawler mechanism for indexi ng web pages after they are downloaded locally.Try to build a relatively independent,highly portable,and versatile web crawler system.Process the process of seed link management,crawl data review,and data indexing and establish a standard API interface.The existing business system triggers the corresponding operation by calling the corresponding API;(2)Research and analyze the advantages and disadvantages of the big data retrieval scheme based on Lucene,and build a relatively independent search s erver to handle the massive operational data in the project,to make up for the shortcomings of the traditional database in processing big data,and to make no changes to the existing business database.Based on the data indexing and search requirements in the big data project platform;(3)Research and analysis of existing artificial intelligence and personalized intelligent recommendation service algorithms,using the user data and gift data of the project to create a user data model,and improve the comm only used personalized intelligent recommendation service algorithm,and ultimately achieve the project based on the gift giving parties User personalized gift accurate recommendation needs.Through the above research and integration of big data full-text search,network data crawler and personalized intelligent data recommendation,this paper innovatively implements a flexible and efficient data aggregation and data intelligence recommendation solution.Firstly,the scheme solves the performance problem of traditional database for big data fuzzy search.Secondly,the scheme adopts the method of secondary indexing of metadata,which not only ensures the security of metadata,but also flexibly adjusts the index structure according to business requirements,which is convenient for the later stage.Data support;Finally,through the establishment of the user model and intelligent recommendation algorithm,the platform reconstruction program achieves the value enhancement at the business level.Finally,through practice,the feasibility and practical value of the research scheme of this paper are proved.At the same time,the program has a wide range of reference for data aggregation and data intelligence recommendation of many big data Internet platforms.