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基于用户反馈及其时效性改进的协同过滤推荐算法--《上海师范大学》2022年硕士论文
Source: cdmd.cnki.com.cn
Article summary:
1. This article proposes a collaborative filtering recommendation algorithm based on user feedback and its timeliness.
2. The proposed algorithm takes into account user non-common ratings, time factors, and user rating preferences to improve the accuracy of predictions.
3. The proposed algorithm combines improved user similarity with user rating preference differences for adaptive integration to generate more accurate prediction results.
Article analysis:
The article is generally reliable and trustworthy in terms of its content and claims made. It provides a detailed overview of the research problem, the current state of research in this field, and the proposed solution to address it. The article also provides a clear explanation of the proposed algorithm, including how it takes into account user non-common ratings, time factors, and user rating preferences to improve the accuracy of predictions. Furthermore, the article provides evidence for its claims by citing relevant literature in this field as well as providing examples from real-world applications.
The only potential bias that could be identified is that the article does not provide any counterarguments or alternative solutions to address the research problem presented in this paper. However, given that this is an academic paper rather than a journalistic piece, it is understandable that counterarguments are not included as part of the discussion.