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TLRank:一种新的社会化协同排序推荐算法

李改 徐清振 李磊 黄锦涛

李改, 徐清振, 李磊, 黄锦涛. TLRank:一种新的社会化协同排序推荐算法[J]. 华南师范大学学报(自然科学版), 2019, 51(5): 121-128. doi: 10.6054/j.jscnun.2019094
引用本文: 李改, 徐清振, 李磊, 黄锦涛. TLRank:一种新的社会化协同排序推荐算法[J]. 华南师范大学学报(自然科学版), 2019, 51(5): 121-128. doi: 10.6054/j.jscnun.2019094
LI Gai, XU Qingzhen, LI Lei, HUANG Jintao. TLRank:A New Social Collaborative Ranking Recommendation Algorithm[J]. Journal of South China normal University (Natural Science Edition), 2019, 51(5): 121-128. doi: 10.6054/j.jscnun.2019094
Citation: LI Gai, XU Qingzhen, LI Lei, HUANG Jintao. TLRank:A New Social Collaborative Ranking Recommendation Algorithm[J]. Journal of South China normal University (Natural Science Edition), 2019, 51(5): 121-128. doi: 10.6054/j.jscnun.2019094

TLRank:一种新的社会化协同排序推荐算法

doi: 10.6054/j.jscnun.2019094
基金项目: 

国家自然科学基金项目 61370186

广东省自然科学基金项目 2016A030310018

广东省科技计划项目 2014A010103040

广东省科技计划项目 2014B010116001

广东省大学生科技创新培育专项资金 pdjh2019a0951

广东省教育厅“创新强校工程”特色创新类项目  2018-KJZX037

详细信息
    通讯作者:

    李改,副教授,Email:ligai999@126.com

  • 中图分类号: TP39

TLRank:A New Social Collaborative Ranking Recommendation Algorithm

  • 摘要: 已有的社会化协同排序推荐算法的研究只是简单地融入用户的社交网络信息,没有考虑用户之间社会化信任网络的传递性;同时,该推荐算法的性能面临数据高度稀疏性问题的挑战.为了进一步解决这些问题,在传统的协同排序推荐算法(ListRank, List-wise Learning to Rank)和最新的社会化协同过滤算法(TrustMF, Social Collaborative Filtering by Trust)的基础上,提出了一种新的社会化协同排序推荐算法(TLRank),融合均高度稀疏的用户的显式评分数据和社会化信任网络数据,以进一步增强协同排序推荐算法的性能.实验结果表明:在各个评价指标下,TLRank算法的性能均优于几个经典的协同排序推荐算法,且复杂度低、运算时间与评分点个数线性相关;TLRank算法的推荐精度高、可扩展性好,适合处理大数据,可广泛运用于互联网信息推荐领域.
  • 图  1  信任规范化参数对TLRank算法性能的影响

    Figure  1.  The impact of the trust regularization parameter in TLRank

    图  2  TLRank算法中用户比例变化对算法运行时间的影响

    Figure  2.  The influence of TLRank running time in terms of the number of users in the training set

    图  3  5种协同排序推荐算法在Epinions数据集上的性能比较

    Figure  3.  The comparison of performance of five collaborative ranking recommendation algorithms based on Epi-nions datasets

    图  4  5种协同排序推荐算法在Ciao数据集上的性能比较

    Figure  4.  The comparison of performance of five collaborative ranking recommendation algorithms based on Ciao datasets

    图  5  5种协同排序推荐算法在Flixster数据集上的性能比较

    Figure  5.  The comparison of performance of five collaborative ranking recommendation algorithms based on Flixster datasets

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出版历程
  • 收稿日期:  2019-04-15
  • 刊出日期:  2019-10-25

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