Location-Aware Personalized News Recommendation Based on Behavior and Popularity Technique

Abstract

Now-a-days people can read news from several sources around the world. This paper investigates a novel user profile model to express users' preferences from different aspects. Then, considers the scope of the user's preferences for historical news, and propose a method to calculate the desire weight of historic news consistent with the user's analyzing behavior and the popularity of news. This method may want to assemble user profiles greater correctly. Additionally, represents a dynamic technique for news recommendation, wherein each short-term and long-term user preferences are taken into consideration. The contribution work is to implement location-aware personalized news recommendation with explicit semantic analysis (LP-ESA), which recommends news using both the users' personal interests and their geographical contexts. The experimental consequences show that BAP technique and LP-ESA technique can fundamentally increase the recommendation outcome.