ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (8): 1631-1643.doi: 10.7544/issn1000-1239.2017.20170128

所属专题: 2017人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

播存网络环境下UCL推荐多样性优化算法

顾梁,杨鹏,董永强   

  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: 2017-08-01
  • 基金资助: 
    国家自然科学基金项目(61472080,61672155);中国工程院咨询研究项目(2015-XY-04);国家“八六三”高技术研究发展计划基金项目(2013AA013503);软件新技术与产业化协同创新中心项目

A Diversified Recommendation Method for UCL in Broadcast-Storage Network

Gu Liang, Yang Peng, Dong Yongqiang   

  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: 2017-08-01

摘要: 播存网络将广播分发模式引入现有互联网体系结构,极大地降低网络共享过程中产生的冗余流量,可有效缓解信息过载问题.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,在UCL个性化推荐过程中,如何结合播存网络的富语义、高时效特征,有效地提高UCL推荐列表的多样性,成为播存网络中一个亟需解决的关键问题.针对播存网络环境的需求,提出了一种基于语义覆盖树的UCL推荐多样性优化算法UDSCT,将该问题分为UCL语义覆盖树构建和多样化UCL列表查询2个步骤.在UCL语义覆盖树构建阶段,基于语义覆盖树的若干约束条件,充分考虑UCL语义信息及非语义用户评分信息,同时,较新的UCL具有较高的优先权,以保证列表的时效性;在多样化UCL列表查询阶段,采用简单树查询及启发式列表补充操作,可快速高效地获得多样性优化后的UCL推荐列表,并可进一步根据用户请求快速返回指定的UCL集合.通过理论分析及一系列仿真实验验证,结果证明:UDSCT算法相对于基准算法能够获得更好的多样性优化效果及效率,可有效满足播存网络环境的需求.

关键词: 播存网络, 统一内容标签, 推荐, 多样性, 时效性

Abstract: By introducing broadcast distribution into TCP/IP, Broadcast-Storage network has clear advantages in reducing the redundant traffic in the Internet and remitting information overload problem. Uniform content label (UCL) is used to express the needs of users and help users obtain the information resources in Broadcast-Storage network. In the process of UCL recommendation, one key problem that needs to be solved is that how to improve the diversity of recommendation based on the features of Broadcast-Storage network, e.g., rich semantic information and high novelty. To solve this problem, this paper proposes a diversification method UDSCT for UCL recommendation based on semantic cover tree. UDSCT consists of two components. The first one is constructing the semantic cover tree for UCLs, which obeys some proposed invariants and considers the semantic information of UCL and the ratings from users. Besides that, new UCLs are given priority to improve the novelty of the whole UCL list. The second component is the query of diversified UCL list, which uses simple tree query and heuristic list supplement operation to obtain the diversified UCL list fast and returns specified UCL sets rapidly according to users’ need. Theoretical analysis and a series of experiments results show that, UDSCT outperforms some benchmark algorithms and is suitable for Broadcast-Storage network.

Key words: Broadcast-Storage network, uniform content label, recommendation, diversity, novelty

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