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提出一种融合用户偏好优化聚类的协同过滤推荐算法。首先利用RP-IIP算法形成细粒度用户-项目类型偏好矩阵,真实反映出用户兴趣偏好并缓解数据稀疏性;然后在该矩阵上利用蝙蝠优化的用户模糊聚类算法进行聚类,增强了用户的聚类效果并提高可扩展性,从隶属度较高的簇中选取目标用户的最近邻居,提高了最近邻选取的准确性;最后,建立用户加权相似度模型对目标用户进行评分预测并产生推荐,进一步提高推荐结果的准确性。实验结果表明,所提出的算法能够产生更好的推荐结果。
Abstract：A collaborative filtering recommendation algorithm based on optimized clustering with user preference was proposed. Firstly, a fine-grained user-item type preference matrix was formed by using the rating proportion-inverse item proportion algorithm, which truly reflected the user′s interest preference and alleviates the data sparsity. Then, the bat-optimized user fuzzy clustering algorithm was used to cluster on the matrix. The user′s clustering effect was enhanced and the scalability was improved. The nearest neighbor of the target user was selected from the cluster with higher membership degree, and the accuracy of the nearest neighbor selection was improved. Finally, the user weighted similarity model was established to predict the target users and to generate recommendations, which further improved the accuracy of the recommendation results. The experimental results showed that the proposed algorithm could produce better recommendation results.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2019184
中图分类号:TP391.3
引用信息:
[1]李悦,谢珺,侯文丽,等.融合用户偏好优化聚类的协同过滤推荐算法[J],2020,52(02):29-35.DOI:10.13705/j.issn.1671-6841.2019184.
基金信息:
国家自然科学基金项目(61503271);; 山西省自然科学基金项目(201801D121144,201801D221190)