Optimization And Implementation Of The News Individuation Recommendation System Based On Python

Intelligent recommendation as the rapid development of the Internet has become an indispensable technology in daily life,people are more and more accustomed to reading news online to understand the news hot spots.However,various online news are increasing in the number of billions per day.And everyone has their own interests and reading habits.In such a huge amount of news,how to allow users to browse news that they are interested in mass media news,Has become the focus of the current big data industry research.The media company hope to personalize the user through relevant big data algorithms combined with the user's behavior data and news text topic information.Based on the technical difficulties and system weaknesses of the news intelligent recommendation system in the market,this paper improves and designs a set of news personalized intelligent recommendation system.First of all,it is difficult to grasp the difficulty of real-time accurate user hobbies based on past news recommendation systems.The system improves design,adopting an important aspect of collecting user behavior data from multiple angles:first,system combined with related mechanical learning algorithms,analyze the daily behavior data of users crawled from the user's mobile phone and obtain the user's characteristic image;second,by analyzing the user's real-time reading behavior data,the user's real-time interests and hobbies are predicted;the system finally combines the user's portrait and the user's real-time interests and interests.Secondly,System will provide real-time news network data,using the advanced text processing algorithm and combined with the theme of the training system through a lot of news out model,accurately extract the news topic type,combining the user's interests,to recommend the news in a timely manner to which they are interested in them.At last,this system has increased the user reading behavior data feedback link compared to previous news recommendation systems,and the system is recommending real-time news messages to users.The user is provided with real-time feedback on whether the news is read,commented,shared,reproduced,and read time and the customer's interests and hobbies predicted in the system are constantly revised and updated through feedback on the customer's reading behavior information.Changes in user hobbies and hobbies over time result in irrationality of news recommendations,realizing personalized recommendations for real news messages.