Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 448-453.DOI: 10.11772/j.issn.1001-9081.2019081565

• CCF NDBC 2019 • Previous Articles     Next Articles

Activity recommendation method based on directed label graph and user feedback in event-based social network

Xiaohuan SHAN, Zhiguo ZHANG, Baoyan SONG, Chenglin REN()   

  1. School of Information,Liaoning University,Shenyang Liaoning 110036,China
  • Received:2019-08-12 Revised:2019-09-12 Accepted:2019-10-24 Online:2019-11-04 Published:2020-02-10
  • Contact: Chenglin REN
  • About author:SHAN Xiaohuan, born in 1987, Ph. D. candidate, experimentalist. Her research interests include graph data management, database, big data management.
    ZHANG Zhiguo,born in 1991, M. S. candidate. His research interests include big data management.
    SONG Baoyan, born in 1965, Ph. D., professor. Her research interests include database, RFID event stream processing, big data management, graph data management.
  • Supported by:
    National Natural Science Foundation of China(61472169);the Key Research and Development Program of Liaoning Province(2017231011);the Shenyang Young Science and Technology Innovation Talents Support Program(RC180244);the Project of the Liaoning Public Opinion and Network Security Big Data System Engineering Laboratory(04-2016-0089013);the Scientific Research Project of Liaoning Provincial Education Department(LYB201617)

事件社交网中基于有向标签图及用户反馈的活动推荐方法

单晓欢, 张志国, 宋宝燕, 任成林()   

  1. 辽宁大学 信息学院,沈阳 110036
  • 通讯作者: 任成林
  • 作者简介:单晓欢(1987—),女,辽宁沈阳人,实验师,博士研究生,CCF会员,主要研究方向:图数据处理、数据库、大数据管理
    张志国(1991—),男,山东临沂人,硕士研究生,主要研究方向:大数据管理
    宋宝燕(1965—),女,辽宁开原人,教授,博士,CCF高级会员,主要研究方向:数据库、射频识别事件流处理、大数据管理、图数据管理;
  • 基金资助:
    国家自然科学基金资助项目(61472169);辽宁省重点研发计划项目(2017231011);沈阳市中青年科技创新人才支持计划项目(RC180244);辽宁省公共舆情与网络安全大数据系统工程实验室资助项目(04-2016-0089013);辽宁省教育厅科学研究项目(LYB201617)

Abstract:

Due to the timeliness of activities in Event-Based Social Network (EBSN), the traditional social network recommendation algorithms cannot be applied to EBSN. In addition, most of the traditional recommendation algorithms ignore the feedback that can affect whether the previous users accept the recommendation, which influences subsequent recommendation quality. Therefore, an activity recommendation method based on directed label graph and user feedback in EBSN was proposed. Firstly, EBSN was abstracted into a directed label graph, and a Directed Graph Structure Feature (DGSF) index was construction by extracting the property feature information of nodes and edges to filter nodes and edges for the first time. DGSF index consists of node property feature index, directed edge property feature index and time feature index. Secondly, a multi-attribute candidate set filtering strategy based on DGSF index was proposed. By using the limits of time, in-degrees and out-degrees of nodes, and label types, the further pruning of the candidate sets was realized to avoid redundant computation. Thirdly, an improved UCB (Upper Confidence Bound) activity recommendation algorithm with user feedback was put forward, namely EN_UCB (Elastic Net UCB). In EN_UCB, with the introduction of the elastic net regression, the interest values of the user to the activities were calculated according to many influencing factors, and the activities with high interest values were recommended to the user. At the same time, the feedback whether the user accepted the activities was received to optimize the subsequent user recommendation. Experimental results show that EN_UCB has the accept rate higher than TS (Thompson Sampling), UCB and eGreedy, the regret rate far lower than TS and eGreedy, the running time superior to TS, UCB and eGreedy, and the larger the number of activities, the more obvious the advantages. The proposed method implements online activity recommendation in EBSN effectively.

Key words: Event-Based Social Network (EBSN), directed label graph, user feedback, activity recommendation, elastic network regression

摘要:

由于基于事件的社交网络(EBSN)中的活动具有时效性,传统社交网络推荐算法无法适用于EBSN。此外,大多数算法忽略了能影响后续推荐质量的前用户是否接受活动的反馈意见。为此,提出一种EBSN中基于有向标签图及用户反馈的活动推荐方法。首先,将EBSN抽象为有向标签图,并抽取图节点及边的属性特征信息,构建有向图结构特征(DGSF)索引,该索引由节点属性特征索引、有向边属性特征索引以及时间特征索引构成,利用该索引对节点及边进行初次过滤。其次,提出基于DGSF索引的多属性候选集过滤策略,利用时间、节点的出入度、标签类型等特征的限制,实现对查询图候选集的进一步剪枝,避免冗余计算。然后,提出一种具有用户反馈的改进UCB(Upper Confidence Bound)活动推荐算法——EN_UCB,通过引入弹性网回归,根据多影响因素计算用户对活动的兴趣值,为用户推荐兴趣值高的活动,同时接收用户是否接受该活动的反馈,以优化后续用户的推荐。大量实验结果表明,EN_UCB算法的接受率高于TS(Thompson Sampling)、UCB以及eGreedy算法,遗憾率远远低于TS和eGreedy算法,且运行效率高于TS、UCB以及eGreedy算法,活动数越大,优势越明显。所提算法能有效实现EBSN上的在线活动推荐。

关键词: 基于事件的社交网络, 有向标签图, 用户反馈, 活动推荐, 弹性网回归

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