个性化推荐系统中协同过滤推荐算法优化研究

协同过滤推荐算法是目前个性化推荐系统中应用比较广泛的一种算法。然而,它在处理数据稀疏性、可扩展性等方面存在一定不足。针对数据稀疏性问题,本文首先基于Slope One算法对初始的评分矩阵进行缺失值填充,其次利用基于K-means聚类的协同过滤算法预测目标用户的评分,并结合MovieLens数据集给出了相关对比实验;针对扩展性问题,本文首先提出了一种基于中心聚集参数的改进K-means算法,其次,给出了基于中心聚集参数改进K-means的协同过滤推荐算法流程,并结合MovieLens数据集设计了相关对比实验。实验结果表明,本文所提方法推荐精度均得到显著提高,数据稀疏性和扩展性问题得到了有效改善。因此,本文的研究结论不仅可进一步丰富协同过滤推荐算法的现有理论成果,还可以为提高推荐系统的精度提供理论依据和决策参考。

Research on Collaberative Filtering Recommendation Algorithm Optimization in Personalized Recommendation

Collaborative filtering recommendation algorithm is widely used in personalized recommendation system. However, it has some shortcomings in data sparseness and scalability. This paper proposes a novel approach to solve the problems, which contains two components. The first component is to solve the problem of data sparseness by filling the initial scoring matrix with missing values based on Slope One algorithm and using the collaborative filtering algorithm based on K-means clustering to predict the score of target users. The second one is to solve the scalability problem by proposing an improvedimproveu K-means algorithm based on central aggregation parameters. The results of the relevant comparative experiments on Movielens dataset show that the recommended precision is significantly improved and the data sparseness and scalability issues are effectively improved. Therefore, the research conclusions of this paper can not only further enrich the existing theoretical achievements of collaborative filtering recommendation algorithm, but also provide theoretical basis and decision-making reference for improving the accuracy of the recommendation system.