ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (2): 475-486.doi: 10.7544/issn1000-1239.2015.20131418

• 信息处理 • 上一篇    下一篇

一种播存网络环境下的UCL协同过滤推荐方法

顾梁,杨鹏,罗军舟   

  1. (东南大学计算机科学与工程学院 南京 211189) (计算机网络和信息集成教育部重点实验室(东南大学) 南京 211189) (guliang@seu.edu.cn)
  • 出版日期: 2015-02-01
  • 基金资助: 
    基金项目:国家“八六三”高技术研究发展计划基金项目(2013AA013503);国家自然科学基金项目(61472080);中国工程院咨询研究项目(2013-XY-6)

A Collaborative Filtering Recommendation Method for UCL in Broadcast-Storage Network

Gu Liang, Yang Peng, Luo Junzhou   

  1. (School of Computer Science and Engineering, Southeast University, Nanjing 211189) (Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
  • Online: 2015-02-01

摘要: 信息资源在分发共享过程中存在带宽拥塞、内容冗余等问题,播存网络借助“一点对无限点”的物理广播分发共享信息资源,对解决此类问题有独特优势.播存网络采用统一内容标签(uniform content label, UCL)适配用户兴趣和推荐信息资源,用户如何高效地获得自己感兴趣的UCL是播存网络中的关键问题.针对该问题,提出一种播存网络环境下的UCL协同过滤推荐方法(unifying collaborative filtering with popularity and timing, UCF-PT).首先,通过设定一对相似度阈值来计算用户与UCL数据的稀疏情况,根据稀疏情况决定二者对UCL评分的影响权值,并基于二者权值预测用户对UCL的评分,生成推荐结果集.其次,依据UCL热度调整推荐结果集的UCL顺序,从而使热门UCL更容易推荐给用户;最后提出UCL价值衰减函数,保证较新的UCL具备较高的推荐优先级.实验结果表明:与传统推荐方法相比,该方法不仅具有良好的推荐精度,还可保证所推荐UCL的热度与时效性,更适用于在播存网络环境下推荐UCL.

关键词: 播存网络, 统一内容标签, 推荐, 协同过滤, 预测

Abstract: Problems like bandwidth congestion, content redundancy exist in the sharing of information resources. Broadcast-Storage network has a particular advantage in solving these issues because of its unique feature of one to infinite by physical broadcast. Uniform content label, UCL, is used to express the needs of users and help users understand the information resources in Broadcast-Storage environment. Due to UCL’s large quantity, how to guide users to get their preferred UCLs efficiently is quite significant. To address this problem, this paper proposes a unifying collaborative filtering method with popularity and timing (UCF-PT) for UCL recommendation. First, a pair of thresholds are set to estimate the sparsity of users and UCLs in the dataset and determine the weights of users and UCLs in recommendation. Then UCF-PT predicts the ratings of users on UCLs based on the weights and generates a recommendation list. Moreover, the method makes popular and new UCLs more likely to be recommended by considering UCL popularity and using exponential decay in recommendation. Experiments show that, compared with traditional recommendation methods, the method proposed in this paper possesses better recommendation accuracy and ensures the popularity and novelty of recommended UCLs. Therefore, it is more suitable for recommending UCLs in Broadcast-Storage environment.

Key words: Broadcast-Storage network, uniform content label, recommendation, collaborative filtering, predict

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