Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Abstract

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

Design of Personalized News Recommendation System Based on an Improved User Collaborative Filtering Algorithm

“…Wu et al [9] proposed a cooperative noise reduction coder to recommend to users, where the automatic noise reduction encoder formulates the user project feedback data to generate a distributed structure of users and projects. Han [10] improved the two stages of data preprocessing and nearest neighbor selection in the CF algorithm, flled the user project evaluation matrix, introduced the tag factor and time factor, integrated CF with dichotomy K-means, and improved the similarity calculation formula. Saranya and Sadasivam [11] adopted a CF algorithm based on rough sets to score news categories, which improve the ranking of novel news.…”

Personalized News Recommendation: Methods and Challenges

“…Feature-based news modeling methods mainly rely on handcrafted features to represent news articles. In most CF-based methods, news articles are represented by their IDs [23,49,60,130,133,168]. However, on most news websites novel news are published quickly and old ones will soon vanish.…”

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

“…AMF has been widely used as a classic excellent filter for filtering salt and pepper noise [21][22][23][24]. Y i represents the coordinate window of a rectangle of size L * L, and L max * L max is the maximum size of the rectangle window during the algorithm.…”