计算机工程 ›› 2020, Vol. 46 ›› Issue (10): 88-94,102.doi: 10.19678/j.issn.1000-3428.0055939

• 人工智能与模式识别 • 上一篇    下一篇

改进的蜂群优化聚类集成联合相似度推荐算法

王岩, 王聪英, 申艳梅   

  1. 河南理工大学 计算机科学技术学院, 河南 焦作 454000
  • 收稿日期:2019-09-06 修回日期:2019-10-18 发布日期:2019-11-08
  • 作者简介:王岩(1980-),男,讲师、博士,主研方向为数据挖掘、信息系统建模、商务智能;王聪英,硕士研究生;申艳梅,教授、博士。
  • 基金项目:
    国家自然科学基金(61502150);河南省高等学校重点科研项目(16A120013);河南理工大学博士基金(B2015-42)。

Clustering Ensemble Joint Similarity Recommendation Algorithm Optimized by Improved Bee Colony

WANG Yan, WANG Congying, SHEN Yanmei   

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
  • Received:2019-09-06 Revised:2019-10-18 Published:2019-11-08

摘要: 协同过滤算法由于推荐效果良好,而被广泛应用于推荐领域,但其在数据稀疏及冷启动的情况下会导致推荐效果明显下降。在数据稀疏情况下,为充分利用用户的历史信息以提高算法的推荐精度,提出一种改进的聚类联合相似度推荐算法。采用改进的蜂群算法来优化K-means++聚类的中心点,使聚类中心在整个数据内达到最优,并对聚类结果进行集成,使得聚类得到进一步优化。根据聚类结果,在同一类中采用改进的用户相似度算法来优化传统相似度算法,使用户间的相似度达到最优,并根据领域的评分预测方法将最佳结果推荐给用户。实验结果表明,该算法的精度、召回率及平均绝对误差均优于其他现有算法,且在数据稀疏情况下其性能仍最佳。

关键词: 联合相似度, 集成聚类, K-means++聚类, 协同过滤, 人工蜂群, 邻域搜索

Abstract: The collaborative filtering algorithm is widely used in the field of recommendation because of its good recommendation effect,which nonetheless is significantly reduced when the data is sparse and from a cold start.In this case,in order to make full use of the user’s historical information to improve the recommendation precision,this paper proposes an improved clustering joint similarity recommendation algorithm.The center point of K-means++clustering is improved by using the bee colony algorithm,so that the cluster center in the whole data is optimal,and the clustering results are integrated to further optimize the clustering.According to the clustering results,the improved user similarity algorithm is used to optimize the traditional similarity algorithm in the same class,so that the similarity between users is optimal.Then the optimal results are recommended to users according to the score prediction method in the field.Experimental results show that the proposed algorithm outperforms other existing algorithms in terms of the precision,recall rate and Mean Absolute Error(MAE),and its performance is still the best in the case of sparse data.

Key words: joint similarity, ensemble clustering, K-means++clustering, collaborative filtering, artificial bee colony, neighborhood search

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