Abstract
Targeted advertising is a dominant form of online advertising. It considers advertisers’ major concern of their customers, including the consumers’ certain traits, interests and individual preferences. To promote the effectiveness of advertisement delivery, advertising analysts need to understand advertiser delivery behavior and problems in targeting structure. However, statistical methods cannot meet analytical requirements completely, and analysts have to spend a lot of time reading countless data reports. Concretely, there is no efficient tool accomplishing analysis tasks such as exploring targeting usage at different levels, discovering useful or abnormal targeting combination patterns, finding competition from user behavior. In this paper, we design and implement an interactive visual analytics system named TargetingVis to visualize targeted advertising delivery data to face the challenges. After conducting a detailed requirements analysis with the domain experts from Tencent Inc., we design TargetingVis with four linked views: a novel chord diagram for cross-level exploration of targeting relations, a view for delving into the analysis of targeting combination patterns, an auxiliary view for displaying data indicators and a view to help gain insights into the behavior of advertisers. Finally, we evaluate the usability and efficiency through experiments based on real-world datasets.
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References
Addis A, Armano G, Giuliani A, Vargiu E (2010) A recommender system based on a generic contextual advertising approach. In: Proceedings of the 15th IEEE symposium on computers and communications, pp 859–861. https://doi.org/10.1109/ISCC.2010.5546548
Armano G, Giuliani A, Vargiu E (2011) Studying the impact of text summarization on contextual advertising. In: 2011 database and expert systems applications, pp 172–176. https://doi.org/10.1109/DEXA.2011.78
Bergemann D, Bonatti A (2011) Targeting in advertising markets: implications for offline versus online media. RAND J Econ 42(3):417–443. https://doi.org/10.1111/j.1756-2171.2011.00143.x
Blascheck T, Beck F, Baltes S, Ertl T, Weiskopf D (2016) Visual analysis and coding of data-rich user behavior. In: 2016 IEEE conference on visual analytics science and technology, pp 141–150. https://doi.org/10.1109/VAST.2016.7883520
Chen Y, Pavlov D, Canny JF (2009) Large-scale behavioral targeting. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 209–218. https://doi.org/10.1145/1557019.1557048
Chen H, Chen W, Mei H, Liu Z, Zhou K, Chen W, Gu W, Ma K (2014) Visual abstraction and exploration of multi-class scatterplots. IEEE Trans Vis Comput Graph 20(12):1683–1692. https://doi.org/10.1109/TVCG.2014.2346594
Chen Y, Zhang X, Feng Y, Liang J, Chen H (2015) Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data. J Vis 18(2):237–254. https://doi.org/10.1007/s12650-014-0269-3
Chen W, Lu J, Kong D, Liu Z, Shen Y, Chen Y, He J, Liu S, Qi Y, Wu Y (2017) GameLifeVis: visual analysis of behavior evolutions in multiplayer online games. J Vis 20(3):651–665. https://doi.org/10.1007/s12650-016-0416-0
Chen Q, Yue X, Plantaz X, Chen Y, Shi C, Pong TC, Qu H (2018) ViSeq: visual analytics of learning sequence in massive open online courses. IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2018.2872961
Chen S, Chen S, Wang Z, Liang J, Wu Y, Yuan X (2019a) D-Map+: interactive visual analysis and exploration of ego-centric and event-centric information diffusion patterns in social media. ACM TIST 10(1):11:1–11:26
Chen W, Xia J, Wang X, Wang Y, Chen J, Chang L (2019b) Relationlines: visual reasoning of egocentric relations from heterogeneous urban data. ACM TIST 10(1):2:1–2:21. https://doi.org/10.1145/3200766
Gou L, Zhang XL (2011) Treenetviz: revealing patterns of networks over tree structures. IEEE Trans Vis Comput Graph 17(12):2449–2458. https://doi.org/10.1109/TVCG.2011.247
Hofmann J, Größler M, Rubio-Sánchez M, Pichler P, Lehmann DJ (2017) Visual exploration of global trade networks with time-dependent and weighted hierarchical edge bundles on GPU. Comput Graph Forum 36(3):273–282. https://doi.org/10.1111/cgf.13186
Hojjat SA, Turner J, Cetintas S, Yang J (2017) A unified framework for the scheduling of guaranteed targeted display advertising under reach and frequency requirements. Oper Res 65(2):289–313. https://doi.org/10.1287/opre.2016.1567
Holten D (2006) Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans Vis Comput Graph 12(5):741–748. https://doi.org/10.1109/TVCG.2006.147
Jin Y, Seipp K, Duval E, Verbert K (2016) Go with the flow: effects of transparency and user control on targeted advertising using flow charts. In: Proceedings of the international working conference on advanced visual interfaces, pp 68–75. https://doi.org/10.1145/2909132.2909269
Lu M, Meng D, Peng Y, Li Y, Zhao Y, Fan X, Zhou F (2016) A visual analytics approach for exploring individual behaviors in smartphone usage data. In: 2016 IEEE pacific visualization symposium, pp 224–228. https://doi.org/10.1109/PACIFICVIS.2016.7465275
Malheiros M, Jennett C, Patel S, Brostoff S, Sasse MA (2012) Too close for comfort: a study of the effectiveness and acceptability of rich-media personalized advertising. In: CHI conference on human factors in computing systems, pp 579–588. https://doi.org/10.1145/2207676.2207758
Nguyen PH, Henkin R, Chen S, Andrienko NV, Andrienko GL, Thonnard O, Turkay C (2020) VASABI: hierarchical user profiles for interactive visual user behaviour analytics. IEEE Trans Vis Comput Graph 26(1):77–86. https://doi.org/10.1109/TVCG.2019.2934609
O’Donnell K, Cramer H (2015) People’s perceptions of personalized ads. In: Proceedings of the 24th international conference on world wide web companion, pp 1293–1298. https://doi.org/10.1145/2740908.2742003
Saia R, Boratto L, Carta S (2015) A latent semantic pattern recognition strategy for an untrivial targeted advertising. In: 2015 IEEE international congress on big data, pp 491–498. https://doi.org/10.1109/BigDataCongress.2015.78
Vargiu E, Giuliani A, Armano G (2013) Improving contextual advertising by adopting collaborative filtering. TWEB 7(3):13:1–13:22. https://doi.org/10.1145/2516633.2516635
Vehlow C, Beck F, Weiskopf D (2016) Visualizing dynamic hierarchies in graph sequences. IEEE Trans Vis Comput Graph 22(10):2343–2357. https://doi.org/10.1109/TVCG.2015.2507595
Wang G, Zhang X, Tang S, Zheng H, Zhao BY (2016) Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI conference on human factors in computing systems, pp 225–236. https://doi.org/10.1145/2858036.2858107
Yan J, Liu N, Wang G, Zhang W, Jiang Y, Chen Z (2009) How much can behavioral targeting help online advertising? In: Proceedings of the 18th international conference on world wide web, pp 261–270. https://doi.org/10.1145/1526709.1526745
Yue X, Shu X, Zhu X, Du X, Yu Z, Papadopoulos D, Liu S (2019) Bitextract: interactive visualization for extracting bitcoin exchange intelligence. IEEE Trans Vis Comput Graph 25(1):162–171. https://doi.org/10.1109/TVCG.2018.2864814
Zhang Z, Wang B, Ahmed F, Ramakrishnan IV, Zhao R, Viccellio A, Mueller K (2013) The five Ws for information visualization with application to healthcare informatics. IEEE Trans Vis Comput Graph 19(11):1895–1910. https://doi.org/10.1109/TVCG.2013.89
Acknowledgements
We would like to thank the domain experts from Tencent Inc. and anonymous reviewers for the valuable suggestions. We also thank Dongxin Wei, Yanan Li, Xiaoxiao Xiong, Jing Liang and Qinglai He for their participation. This work was supported by Tencent Rhino-Bird Focused Research Program (18H0487).
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Peng, D., Tian, W., Zhu, M. et al. TargetingVis: visual exploration and analysis of targeted advertising data. J Vis 23, 1113–1127 (2020). https://doi.org/10.1007/s12650-020-00671-w
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DOI: https://doi.org/10.1007/s12650-020-00671-w