[1]李慧,马小平,胡云,等.融合上下文信息的社会网络推荐系统[J].智能系统学报,2015,10(02):293-300.[doi:10.3969/j.issn.1673-4785.201406017]
 LI Hui,MA Xiaoping,HU Yun,et al.Social network recommendaton system mixing contex information[J].CAAI Transactions on Intelligent Systems,2015,10(02):293-300.[doi:10.3969/j.issn.1673-4785.201406017]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
293-300
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
Social network recommendaton system mixing contex information
作者:
李慧12 马小平1 胡云2 施珺2
1. 中国矿业大学 信电学院, 江苏 徐州 221008;
2. 淮海工学院 计算机工程学院, 江苏 连云港 222005
Author(s):
LI Hui12 MA Xiaoping1 HU Yun2 SHI Jun2
1. School of Information & Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China;
2. Department of Computer Science, Huaihai Institute of Technology, Lianyungang 222005, China
关键词:
上下文信息社会网络矩阵因式分解推荐协同过滤
Keywords:
contextinformationsocial networkmatrix factorizationrecommendationcollaborative filtering
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201406017
文献标志码:
A
摘要:
上下文环境和社会网络信息已经成为推荐系统所需的重要信息来源,在推荐系统中融入这些信息将进一步改进推荐系统的精度和用户满意度。为了提高用户对推荐系统的满意度,提出一种融入上下文信息与社交网络信息的个性化推荐系统CS。该算法应用随机决策树划分原始的用户-商品评分矩阵来进行上下文信息的处理,使得具有相似上下文信息的评分被分为一组。随后应用矩阵因式分解来预测用户对未评分项的预测。为了整合社交网络信息,在考虑上下文信息的环境下提出了一种融入社会网络关系的增强推荐模型,使用一种基于信任度的皮尔逊相关系数来衡量用户的相似度。在真实的实验数据集上进行验证,表明CS系统推荐较传统的基于上下文的和基于社会网络的推荐算法在性能上和推荐性能上有了很大的改善。
Abstract:
Contexts and social network information is valuable information for building an accurate recommender system. The merging of such information could further improve accuracy of the system and user satisfaction. This paper proposes the context and social (CS) network, which is novel context-aware recommender system incorporating elaborately processed social network information, in order to increase the user satisfaction on the recommendation system. The contextual information happens by applying random decision trees to partition the original user-item-rating matrix such that the ratings with similar contexts are together. The matrix factorization functionality is to predict missing preference of a user for an item using the partitioned matrix. An enhanced recommendation model aided by social relationships considering the context information is proposed. A trust-based Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments showed that CS enhances its performance compared with traditional recommendation algorithms based on context and social networks.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-6-11;改回日期:。
基金项目:国家自然科学基金资助项目(61403156,61403155);江苏省高校自然科学基金资助项目(13KJB520002,14KJB520005).
作者简介:李慧,女,1979年生,讲师,博士研究生,主要研究方向为智能信息处理、社会网络分析、计算机技术及应用。主持并完成江苏省自然科学基金1项,参与国家自然科学基金2项,出版专著1部,发表学术论文20余篇;马小平,男,1961年生,教授,博士,主要研究方向为控制理论及应用、计算机技术及应用。主持并完成多项科研项目,其中国家“863”项目1项、国家自然科学基金项目2项、江苏省自然科学基金项目3项、江苏省高校基础研究项目2项、大型企业横向科研项目20余项;胡云,女,1978年生,副教授,主要研究方向为复杂网络分析理论及应用、数据挖掘、多Agent系统。主持并完成科研项目多项,其中国家自然科学基金项目1项,江苏省自然科学基金项目1项,参与出版专著1部,发表学术论文10余篇。
通讯作者:李慧.E-mail:shufanzs@126.com.
更新日期/Last Update: 2015-06-15