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.
Highlights
With the deepening of informatization and rapid changes in network technology, the era of information explosion is coming one after another, and the ways for users to obtain information are becoming more abundant [1, 2]
E content-based recommendation algorithm first models real-time news, builds a user interest model based on the browsing information of a specific user, and recommends news events that are similar to the target user’s interest model but are not included in the browsing history [5, 6]
In the Tapestry system, users can determine the type of e-mail based on their own interests and can decide whether to read this e-mail based on the label of the e-mail [9]. e system cannot actively recommend according to the user’s interest
Summary
With the deepening of informatization and rapid changes in network technology, the era of information explosion is coming one after another, and the ways for users to obtain information are becoming more abundant [1, 2]. We have entered the era of information overload from the era of information scarcity At this time, how to accurately and efficiently filter out the content that users are really interested in from the dazzling information has become more and more important [4]. E content-based recommendation algorithm first models real-time news, builds a user interest model based on the browsing information of a specific user, and recommends news events that are similar to the target user’s interest model but are not included in the browsing history [5, 6]. E label factor and time factor are introduced, the similarity measurement formula is improved, and an improved collaborative filtering recommendation algorithm is obtained. A simulated data set and a real data set were selected to test the performance of the algorithm and compared with the traditional user-based collaborative filtering recommendation algorithm. On the real data set, this paper first tested the pros and cons of the three similarity schemes and selected a similarity scheme to test the algorithm in this paper and the traditional algorithm under different neighbors
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