Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony

GUO Lei; HU Yan

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Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony

更新时间：2026-09-04

- Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony
- Vol. 34, Issue 6, Pages: 31-35(2017)
- 作者机构：
  
  武汉理工大学计算机科学与技术学院,湖北,武汉,430070
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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郭磊, 胡燕. 基于人工蜂群的项聚类推荐算法[J]. 微电子学与计算机, 2017, 34(6): 31-35.

GUO Lei, HU Yan. Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony[J]. 2017, 34(6): 31-35.
郭磊, 胡燕. 基于人工蜂群的项聚类推荐算法[J]. 微电子学与计算机, 2017, 34(6): 31-35. DOI：

GUO Lei, HU Yan. Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony[J]. 2017, 34(6): 31-35. DOI：

摘要

旨在改善协同过滤推荐算法中暴露的推荐准确性和实时性问题

提出基于人工蜂群的项聚类推荐算法.在数据处理过程中利用人工蜂群聚类算法对项目聚类

从相似性最高的几个聚类中搜索目标项目最近邻居

以此剔除相似性较低项目的干扰提高推荐准确性

同时也大大缩小项目空间

提高了推荐实时性; 在评分预测过程中采用时间权重函数优化传统评分预测模型以提高评分预测的准确性

从而提高推荐准确性.实验结果表明

推荐的准确性和实时性得到了有效提高.

Abstract

Aiming at improving the accuracy and real-time problem exposing in collaborative filtering recommendation algorithm

A new Item-clustering recommendation algorithm based on artificial bee colony algorithm is proposed. In the process of data processing

the artificial bee colony clustering algorithm is used to cluster the items

Search for the nearest neighbors of the target item from the highest similarity of several clusterings

in order to eliminate the interference of similar items to improve the accuracy of the recommendation

At the same time

it also greatly reduces the item space and improves the real-time recommendation; In the process of scoring

the time weighting function is used to optimize the traditional score prediction model in order to improve the accuracy of the prediction

so as to improve the accuracy of recommendation. The experimental results show that the accuracy and real-time performance are improved effectively.

关键词

Keywords

references

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秦全德, 程适, 李丽, 等. 人工蜂群算法研究综述[J]. 智能系统学报, 2014, 9(2): 127-135. DOI: 127

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