Abstract
This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.
Highlights
Is process can be more clearly defined as follows: for user u, in a specific scenario c, a function is constructed, that is, the recommendation method f(u, i, c) is built to predict the user’s interest in the candidate itemset i. en, all the candidate items are sorted according to the degree of interest, and a recommendation list is generated. We can divide this process into two parts, that is, in actual practice, we have to solve two problems. e first is the problem of data and information, that is, user information, item information, scene information, what this information refers to, and how to process it. e second is the problem of algorithm selection as there are many algorithms, and it is not clear which one should be chosen. is is the core of personalized recommendation because different algorithms may produce different recommendations [14, 15]. erefore, before evaluating any recommendation system, the first thing to evaluate is its recommendation algorithm
With the continuous development of the internet, there is a massive amount of information coming to users. e problem of information overload is becoming increasingly prominent
Based on the above background, it is believed that personalized recommendation approaches can effectively alleviate this problem
Summary
To recommend more interesting items to users, improve the satisfaction in recommendations, achieve true personalization, and achieve “thousands of Complexity people, thousands of needs,” a lot of research is still required [5,6,7]. In this case, computational social system theory would be a useful method. As a product, are relatively stable in a period It will be affected by the time factor, but books and news have something in common in terms of recommendation: both need significant personalization, and the number of products is relatively large. It will be affected by the time factor, but books and news have something in common in terms of recommendation: both need significant personalization, and the number of products is relatively large. erefore, personalized book recommendation research can be used for reference and inspiration for other personalized recommendation research fields
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