This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.

中文翻译：

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推荐系统优化与仿真中基于内容和协同过滤的混合算法

本文研究和研究了基于内容过滤和用户协同过滤的推荐技术，提出了一种基于内容和用户协同过滤的混合推荐算法。该方法不仅利用了内容过滤的优点，而且还可以对所有项目进行相似度匹配过滤，特别是当项目没有被任何用户评价时，可以过滤掉并推荐给用户，从而避免了问题。早期的水平。同时，该方法还利用了协同过滤的优势。当用户数量多，评价水平高时，协同过滤预测的用户评价数据矩阵将变得相对密集，可以减少矩阵的稀疏性，使协同过滤更加准确。这样，通过两者的集成，系统性能将大大提高。在改进的协同过滤算法的基础上，提出了一种基于内容和改进的协同过滤的混合算法。通过将用户评分与项目特征相结合，建立了用户特征评分矩阵来代替传统的用户项目评分矩阵。对用户集执行K均值聚类并提出建议。改进后的算法可以解决传统协同过滤算法数据稀疏的问题。同时，对于新项目，还可以根据项目特征和用户特征评分矩阵的匹配，预测可能对新项目感兴趣的用户，并生成推送列表，有效解决了“冷启动”中新项目的问题。 。” 实验结果表明，本文提出的改进算法在解决数据稀疏，冷启动和在线推荐的速度瓶颈问题上具有重要作用，可以保证更好的推荐质量。