基于点击率预测的微信公众号广告精准投放研究
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摘要
[目的/意义]旨在将微信公众号在社会网络中的位置量化,对微信公众号广告的点击率进行预测,为微信公众号广告的精准投放提供新思路,改善广告的投放效果。[方法/过程]研究基于广告点击率预测模型,利用层次分析法构建微信公众号位置综合评价体系并确定权重,进而结合社会网络分析方法计算评价指标的具体数值,对微信公众号的广告点击率进行预测来指导广告投放。[结果/结论 ]公众号在社会网络中的位置影响其广告点击率,进而影响广告投放效果。广告投放应将公众号在社会网络中的位置考虑在内,以预测广告点击率为指导,避免低迷点,对处于不同位置的公众号采取不同的投放策略。
[Purpose/significance] This paper aims to quantify the position of WeChat public number in the social network, forecast the click rates of WeChat public number ads, and provide implementation ideas for the accurate delivery of WeChat public number ads and improve the effectiveness of advertising. [Method/process] Based on the advertising CTR prediction model, we used the analytic hierarchy process to build a comprehensive evaluation system for the location of WeChat public number and determine the weights. Then we used the social network analysis method to calculate the specific value of the evaluation index and the click rate of the WeChat public number, so that can make predictions to guide ad serving. [Result/conclusion] The position of the public number in social networks affects its CTR, which in turn affects the effectiveness of advertising. The placement of public accounts in social networks should be taken into account in ad serving. We should take the predicted click-through rate as a guide, so as to avoid downturns and adopt different delivery strategies for public numbers in different locations.
[Purpose/significance] This paper aims to quantify the position of WeChat public number in the social network, forecast the click rates of WeChat public number ads, and provide implementation ideas for the accurate delivery of WeChat public number ads and improve the effectiveness of advertising. [Method/process] Based on the advertising CTR prediction model, we used the analytic hierarchy process to build a comprehensive evaluation system for the location of WeChat public number and determine the weights. Then we used the social network analysis method to calculate the specific value of the evaluation index and the click rate of the WeChat public number, so that can make predictions to guide ad serving. [Result/conclusion] The position of the public number in social networks affects its CTR, which in turn affects the effectiveness of advertising. The placement of public accounts in social networks should be taken into account in ad serving. We should take the predicted click-through rate as a guide, so as to avoid downturns and adopt different delivery strategies for public numbers in different locations.
引文
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[20]姜鑫.组织内非正式网络中心性对隐性知识共享的影响研究[J].图书情报工作, 2011, 55(16):111-148.
[21]张碉,朱庆华.学术博客交流网络的核心—边缘结构分析实证研究[J].图书情报工作, 2009, 53(12):25-29.
[2]张荣荣.微信公众号广告投放策略及营销模式探析[J].新闻传播, 2017(13):39-40.
[3]冀芳,张夏恒.微信公众平台传播效果评价研究[J].情报理论与实践, 2015, 38(12):77-81
[4]方靖,陆伟.微信公众号信息传播热度的影响因素实证研究[J].情报杂志, 2016, 35(2):157-162
[5]吴中堂,刘建徽,唐振华.微信公众号信息传播的影响因素研究[J].情报杂志, 2015, 34(4):122-126.
[6]SHAN S, LIU M, XU X. Analysis of the key influencing factors of haze information dissemination behavior and motivation in WeChat[J]. Information discovery and delivery, 2017, 45(1): 21-29
[7]徐宝达,赵树宽,张健.基于社会网络分析的微信公众号信息传播研究[J].情报杂志, 2017, 36(1):120-126
[8]秦泽宇,杨君.微信公众号的广告价值与营销[J].青年记者, 2016(9):99.
[9]AMIT B,PURUSHOTTAM P.Identifying locations for targeted advertising on the Internet[J].International Journal of Electronic Commerce, 2001, 5(3):23-44.
[10]AGICHTEIN E,BRILL E,DUMAIS S.Improving web search ranking by incorporating user behavior information[C]//Proceedings of the 29th annual international ACM SIGIR con-ference on research and development in information retrieval. New York:ACM,2006:19-26.
[11]张迪.一种基于用户行为的定向广告投放算法概述[J].电子制作, 2015(2):139-140.
[12]程龙龙.基于LDA的行为定向广告投放算法研究[D].沈阳:辽宁大学, 2014.
[13]VANESSA M,MASSIMILIANO C,VASSILIS P. A noisy channel approach to contextual advertising[C]//Proceedings of the 1s tinternational workshop on data mining and audience intelligence for advertising.New York:ACM, 2007:21-27.
[14]CIARAMITA M,MURDOCK V,PLACHOURASV.Semantic associations for contextuala dvertising[J].Journal of electronic commerce research, 2008, 9(1):1-15.
[15]BRODER A,FONTOURA M,JOSIFOVSKI V,et al.A seman-tic approach to contextual advertising[C]//Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. New York:ACM, 2007:559-566.
[16]纪文迪,王晓玲,周傲英.广告点击率估算技术综述[J].华东师范大学学报(自然科学版), 2013(3):2-14.
[17]CHAPELLE O, ZHANG Y. A dynamic bayesian network click model for web search ranking[C]//Proceedings of the 18th international conferenceon Worldwideweb.New York:ACM, 2009:1-10.
[18]GUOF,LIUC,KANNAN A,et al.Clickchainmodel inwebsearch[C]//Proceedings of the 18th international conference on world wide web. New York:ACM, 2009:11-20.
[19]RICHARDSON M,DOMINOWSKA E,RAGNO R.Predicting clicks:estimating the click-through rate for new ads[C]//Proceedings of the 16th international conference on world wide web. New York:ACM, 2007:521-530.
[20]姜鑫.组织内非正式网络中心性对隐性知识共享的影响研究[J].图书情报工作, 2011, 55(16):111-148.
[21]张碉,朱庆华.学术博客交流网络的核心—边缘结构分析实证研究[J].图书情报工作, 2009, 53(12):25-29.