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为解决传统知识推荐领域存在的“重相似、轻关联”的问题，并进一步提升推荐精度，提出将相似度和关联度融合为知识匹配度的应对思路，并利用余弦相似度和Apriori算法计算知识间匹配度；而后，通过谱聚类实施空间压缩以降低遍历时耗；最后，基于聚类结果和预测评分确定知识推荐范畴。采用Movielens数据集进行仿真模拟，结果表明，所提算法优于传统的ICF算法和KNN-ICF算法。
Abstract：To solve the problem of emphasizing similarity and neglecting relevance in the field of traditional knowledge recommendation, and further improve the recommendation accuracy, the idea of integrating similarity and relevance to form knowledge matching degree was put forward. Using cosine similarity and Apriori algorithm, the matching degree between knowledge was calculated.Then, spatial compression was implemented through spectral clustering to reduce traversal time consumption.Finally, the category of knowledge recommendation was determined based on the prediction score and clustering results. Movielens data set was used for simulation, and the results showed that the proposed algorithm was superior to the traditional ICF algorithm and KNN-ICF algorithm.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2022141
中图分类号:TP391.3
引用信息:
[1]张建华,郭启迪,曹子傲,等.基于匹配度和谱聚类的知识推荐研究[J],2023,55(04):1-7.DOI:10.13705/j.issn.1671-6841.2022141.
基金信息:
国家社会科学基金项目(19BTQ035)