Personalization recommendation algorithm for Web resources based on ontology

doi:10.11772/j.issn.1001-9081.2014.11.3135

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3135-3139.DOI: 10.11772/j.issn.1001-9081.2014.11.3135

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Personalization recommendation algorithm for Web resources based on ontology

LIANG Junjie,LIU Qiongni,YU Dunhui

School of Computer and Information Engineering, Hubei University, Wuhan Hubei 430062, China

Received:2014-07-28 Revised:2014-07-30 Online:2014-11-01 Published:2014-12-01
Contact: LIU Qiongni
Supported by:
Project supported by the National Natural Science Foundation of China

基于本体的Web资源个性化推荐算法

梁俊杰¹,刘琼妮²,余敦辉¹

1. 湖北大学计算机与信息工程学院,武汉 430062
2. 湖北大学计算机与信息工程学院, 武汉 430062

通讯作者: 刘琼妮
作者简介:梁俊杰(1974-),女,湖北武汉人,副教授,〖BP(〗硕士生导师,〖BP)〗博士,主要研究方向:数据分析、云计算;刘琼妮(1990-),女,湖北武汉人,硕士研究生,主要研究方向:Web服务发现;余敦辉(1974-),男,湖北武汉人,副教授,〖BP(〗硕士生导师,〖BP)〗博士,CCF会员,主要研究方向:服务计算、大数据。
基金资助:
国家自然科学基金资助项目;湖北省自然科学基金资助项目;武汉市科技攻关计划项目

Abstract

Abstract:

To improve the accuracy of recommended Web resources, a personalized recommendation algorithm based on ontology, named BO-RM, was proposed. Subject extraction and similarity measurement methods were designed, and ontology semantic was used to cluster Web resources. With a user's browser tracks captured, the tendency of preferences and recommendation were adjusted dynamically. Comparison experiments with collaborative filtering algorithm based on situation named CFR-RM and personalized prediction algorithm based on model were given. The results show that BO-RM has relatively stable overhead time and good performance in Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP). The results prove that BO-RM improves the efficiency by using offline data analysis for large Web resources, thus it is practical. In addition, BO-RM captures the users' interest in real-time to updates the recommendation list dynamically, which meets the real needs of users.

摘要：

为提高Web资源推荐的准确度,提出基于本体的Web资源个性化推荐算法(BO-RM)。设计Web资源主题抽取算法和相似性度量方法,利用本体语义推理机制实现资源聚类,在推荐过程中通过实时分析用户浏览行为捕获用户个性化偏好的变化,动态实时推荐内容。与基于情境的协同过滤算法（CFR-RM）和基于模型的个性化预测算法（BM-RM）进行对比,结果显示BO-RM的时间开销相对稳定,在平均排序倒数(MRR)和平均准确率(MAP)上均取得了较好的效果。实验结果表明:BO-RM离线完成海量Web资源的分析聚类,有效提高了运行效率,实用性比较强；BO-RM实时捕捉用户兴趣变化,动态更新推荐列表,更加贴近用户的真实需求。

CLC Number:

TP311.5

LIANG Junjie LIU Qiongni YU Dunhui. Personalization recommendation algorithm for Web resources based on ontology[J]. Journal of Computer Applications, 2014, 34(11): 3135-3139.

梁俊杰刘琼妮余敦辉. 基于本体的Web资源个性化推荐算法[J]. 计算机应用, 2014, 34(11): 3135-3139.

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Personalization recommendation algorithm for Web resources based on ontology

基于本体的Web资源个性化推荐算法

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