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通过聚类可以缩小用户近邻空间，从而一定程度缓解传统协同过滤推荐算法存在的可扩展性问题，但因部分用户丢失了有效邻居而使得推荐精度不高。为解决该问题，结合三支聚类提出了一种新的协同过滤方法。该方法分为线下聚类和线上推荐两个步骤。对用户先进行聚类，进而将用户划为核心用户和边界用户，并对这两类用户分别应用不同的聚类规则进行聚类；然后在目标用户所属的簇中产生一个预测评分，对属于多个簇的用户，则聚合每个簇的评分得到其预测结果。实验结果表明，该方法与现有基于聚类的协同过滤算法相比，能有效地提高推荐精度。
Abstract：Clustering can be used to reduce the users′ neighbor space, thereby alleviating the scalability of traditional collaborative filtering algorithms to a certain extent, but the recommendation accuracy is not high due to the loss of effective neighbors of some users. To solve this problem, a new collaborative filtering method combined with three-way clustering was proposed. The proposed method included two steps: offline clustering and online recommendation. The users were divided into core users and boundary users by clustering, and different clustering rules were applied to these two types of users respectively. The prediction score was calculated based on the cluster the target user belonged to. For a user belonging to multiple clusters, the scores of each cluster were aggregated to obtain its prediction score. Experimental results showed that the proposed method could achieve higher accuracy than the current clustering-based collaborative filtering algorithms.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2021237
中图分类号:TP391.3;O225
引用信息:
[1]康凯,胡军.基于三支聚类的协同过滤推荐方法[J],2022,54(03):22-27.DOI:10.13705/j.issn.1671-6841.2021237.
基金信息:
国家自然科学基金项目(61772096,61876201,61876027);; 重庆市自然科学基金项目(cstc2019jcyj-cxttX0002)