Design of Personalized Push Algorithm of Hot Social News Based on User Interest Model

Abstract

As a result of rapid advances in information technology, the volume of information on the Internet is expanding at a breakneck rate. The World Wide Web has evolved into a vast and intricate information space. People have shifted from information deficiency to information overload. The characteristics of Internet information are dispersion, disorder, and mass. A challenging research topic is how to quickly, accurately, and efficiently extract vital information from vast information resources. Web search is becoming one of the Internet field's study centers and focal points. Traditional web search algorithms focus on the link structure of the web and the hierarchical weight of web pages while ignoring the behavior of users, resulting in some search results that are insufficient and inaccurate. In addition, because each web page's hub value and authority value are calculated iteratively, web search is inefficient and susceptible to dispersion and generalization. This study fully integrates the user's interest behavior and relevant, intelligent optimization algorithms to address the shortcomings of the traditional World Wide Web search algorithm, based on a synthesis and analysis of relevant domestic and international research. A method of user interest model construction and update for news recommendation is proposed to address the problem of user interest model construction and user interest drift in the news recommendation system. Initially, the original user interest model is constructed using a bisection K-means clustering algorithm and a vector space model. Subsequently, the forgetting function is constructed using the Ebbinghaus forgetting curve, and the user interest model is time-weighted to achieve the goal of updating the user interest model. User-based collaborative filtering recommendations and item-based collaborative filtering suggestions serve as the experiment's baseline. The experimental results suggest that the recommendation performance of the original user interest model is enhanced, with the F value increasing by 4%. The modified model's F value has increased by 1.3% compared to the previous version.