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面向众筹平台的个性化推荐进化算法研究
摘要：近年来，众筹平台逐渐成为互联网创业的一种重要方式，但是用户在众筹平台上往往面临信息过载的问题。为了解决这一问题，个性化推荐进化算法逐渐引入到众筹平台中。本文通过对个性化推荐进化算法的研究，提出了一种面向众筹平台的个性化推荐进化算法，为众筹平台用户提供更加准确和个性化的推荐服务。
关键词：众筹平台；个性化推荐；进化算法
1. 引言
随着互联网技术的发展，众筹平台逐渐成为了创业者和投资者进行资金募集和投资的重要途径。然而，随着众筹项目数量的增加，用户在众筹平台上往往面临信息过载的问题，很难找到自己感兴趣又适合参与的项目。因此，个性化推荐技术成为了解决众筹平台用户信息过载问题的有效途径之一。
2. 相关工作
个性化推荐技术在电子商务和社交媒体等领域已经有很多研究和应用。进化算法作为一种重要的个性化推荐方法，通过模拟生物进化的过程来优化个体的适应度和生存能力，从而实现个性化推荐。
3. 个性化推荐进化算法设计
本文提出了一种基于进化算法的个性化推荐方法。首先，需要构建用户项目评分矩阵。然后，通过进化算法将用户的兴趣模型与项目的特征进行匹配，计算出每个项目与用户的匹配度。最后，根据匹配度对项目进行排序，并向用户推荐相应的项目。
4. 实验结果与分析
通过对真实数据集的实验，验证了本方法的可行性和有效性。实验结果表明，与传统的推荐算法相比，本方法能够提供更加准确和个性化的推荐服务。
5. 结论及展望
本文针对众筹平台用户面临的信息过载问题，提出了一种面向众筹平台的个性化推荐进化算法。实验结果表明，该算法能够有效地解决众筹平台用户遇到的信息过载问题，提供更加准确和个性化的推荐服务。未来的研究可以进一步探索该算法的性能优化和扩展应用。
参考文献：
[1] 徐明芳. 面向个性化推荐的混合进化算法研究[D]. 华中科技大学, 2016.
[2] 张瑞. 基于进化算法的个性化推荐研究[D]. 中国科学院大学, 2017.
[3] 陈梁. 基于进化算法的文本分类与个性化推荐研究[D]. 上海交通大学, 2018.
Abstract: The crowdfunding platform has gradually become an important way for Internet entrepreneurship in recent years, but users often face the problem of information overload on crowdfunding platforms. In order to solve this problem, personalized recommendation evolutionary algorithms have been gradually introduced into crowdfunding platforms. This paper proposes a personalized recommendation evolutionary algorithm for crowdfunding platforms, which provides users with more accurate and personalized recommendation services.
Keywords: crowdfunding platform; personalized recommendation; evolutionary algorithm
1. Introduction
With the development of Internet technology, crowdfunding platforms have gradually become an important way for entrepreneurs and investors to raise funds and invest. However, as the number of crowdfunding projects increases, users often face the problem of information overload on crowdfunding platforms, making it difficult to find projects that they are interested in and suitable for participation. Therefore, personalized recommendation technology has become an effective way to solve the problem of information overload for crowdfunding platform users.
2. Related work
Personalized recommendation technology has been widely researched and applied in the fields of e-commerce and social media. Evolutionary algorithms, as an important personalized recommendation method, optimize the fitness and survival ability of individuals by simulating the process of biological evolution, thereby achieving personalized recommendation.
3. Personalized recommendation evolutionary algorithm design
This paper proposes a personalized recommendation method based on evolutionary algorithms. Firstly, it is necessary to construct the user project rating matrix. Then, the user's interest model is matched with the project features through the evolutionary algorithm, and the matching degree between each project and the user is calculated. Finally, the projects are ranked according to the matching degree, and the corresponding projects are recommended to the user.
4. Experimental results and analysis
Through experiments on real datasets, the feasibility and effectiveness of this method are verified. The experimental results show that compared with traditional recommendation algorithms, this method can provide more accurate and personalized recommendation services.
5. Conclusion and future work
This paper proposes a personalized recommendation evolutionary algorithm for crowdfunding platforms to solve the problem of information overload faced by users. The experimental results show that the algorithm can effectively solve the problem of information overload for crowdfunding platform users and provide more accurate and personalized recommendation services. Future research can further explore the performance optimization and application extension of this algorithm.
References:
[1] Xu Mingfang. Research on hybrid evolutionary algorithm for personalized recommendation [D]. Huazhong University of Science and Technology, 2016.
[2] Zhang Rui. Research on personalized recommendation based on evolutionary algorithm [D]. University of Chinese Academy of Sciences, 2017.
[3] Chen Liang. Research on text classification and personalized recommendation based on evolutionary algorithm [D]. Shanghai Jiao Tong University, 2018.