基于深度学习的流行度预测研究综述

曹婍,沈华伟,高金华,程学旗

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中文信息学报 ›› 2021, Vol. 35 ›› Issue (2) : 1-18,32.
综述

基于深度学习的流行度预测研究综述

  • 曹婍1,2,沈华伟1,2,高金华1,程学旗1
作者信息 +

Survey on Deep Learning Based Popularity Prediction

  • CAO Qi1,2, SHEN Huawei1,2, GAO Jinhua1, CHENG Xueqi1
Author information +
History +

摘要

在线社交网络中的消息流行度预测研究,对推荐、广告、检索等应用场景都具有非常重要的作用。近年来,深度学习的蓬勃发展和消息传播数据的积累,为基于深度学习的流行度预测研究提供了坚实的发展基础。现有的流行度预测研究综述,主要是围绕传统的流行度预测方法展开的,而基于深度学习的流行度预测方法目前仍未得到系统性地归纳和梳理,不利于流行度预测领域的持续发展。鉴于此,该文重点论述和分析现有的基于深度学习的流行度预测相关研究,对近年来基于深度学习的流行度预测研究进行了归纳梳理,将其分为基于深度表示和基于深度融合的流行度预测方法,并对该研究方向的发展现状和未来趋势进行了分析展望。

Abstract

Popularity prediction over online social networks plays an important role in various applications, e.g., recommendation, advertising, and information retrieval. Recently, the rapid development of deep learning and the availability of information diffusion data provide a solid foundation for deep learning based popularity prediction research. Existing surveys of popularity prediction mainly focus on traditional popularity prediction methods. To systematically summarize the deep learning based popularity prediction methods, this paper reviews existing popularity prediction methods based on deep learning, categorizes the recent deep learning based popularity prediction research into deep representation based and deep fusion based methods, and discusses the future researches.

关键词

流行度预测 / 深度学习 / 信息传播 / 综述

Key words

popularity prediction / deep learning / information diffusion / survey

引用本文

导出引用
曹婍,沈华伟,高金华,程学旗. 基于深度学习的流行度预测研究综述. 中文信息学报. 2021, 35(2): 1-18,32
CAO Qi, SHEN Huawei, GAO Jinhua, CHENG Xueqi. Survey on Deep Learning Based Popularity Prediction. Journal of Chinese Information Processing. 2021, 35(2): 1-18,32

参考文献

[1] Cao Q, Shen H, Cen K, et al. DeepHawkes: Bridging the gap between prediction and understanding of information cascades[C]//Proceedings of the 26th ACM International Conference on Information and Know-ledge Management. USA: ACM, 2017: 1149-1158.
[2] Shulman B, Sharma A, Cosley D. Predictability of popularity: Gaps between prediction and understanding[C]//Proceedings of the 10th International AAAI Conference on Web and Social Media. USA: AAAI, 2016: 348-357.
[3] Cheng J, Adamic L, Dow P A,et al. Can cascades be predicted?[C]//Proceedings of the 23rd International Conference on World Wide Web. USA: ACM, 2014: 925-936.
[4] Gao X, Cao Z, Li S,et al. Taxonomy and evaluation for microblog popularity prediction[J]. ACM Transactions on Knowledge Discovery from Data, 2019, 13(2): 15-40.
[5] Shen H, Wang D, Song C,et al. Modeling and predicting popularity dynamics via reinforced poisson processes[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. USA: AAAI, 2014: 291-297.
[6] Zhao Q, Erdogdu M A, He H Y,et al. Seismic: A self-exciting point process model for predicting Tweet popularity[C]//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2015: 1513-1522.
[7] Rizoiu M A, Xie L, Sanner S,et al. Expecting to be hip: Hawkes intensity processes for social media popularity[C]//Proceedings of the 26th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee,2017: 735-744.
[8] Sanjo S, Katsurai M. Recipe popularity prediction with deep visual-semantic fusion[C]//Proceedings of the 26th ACM International Conference on Information and Knowledge Management. USA: ACM,2017: 2279-2282.
[9] Zhang W, Wang W, Wang J,et al. User-guided hierarchical attention network for multi-modal social image popularity prediction[C]//Proceedings of the 27th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee,2018: 1277-1286.
[10] 李洋, 陈毅恒, 刘挺.微博信息传播预测研究综述[J].软件学报,2016,27(2): 247-263.
[11] 胡颖, 胡长军, 傅树深, 等. 流行度演化分析与预测综述[J].电子与信息学报,2017,39(4): 805-816.
[12] 胡长军, 许文文, 胡颖, 等. 在线社交网络信息传播研究综述[J].电子与信息学报,2017,39(4): 794-804.
[13] Hofman J M, Sharma A, Watts D J. Prediction and explanation in social systems[J]. Science, 2017, 355(6324): 486-488.
[14] Pinto H, Almeida J M, Gonalves M A. Using early view patterns to predict the popularity of youtube videos[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining. USA: ACM, 2013: 365-374.
[15] Romero DM, Tan C, Ugander J. On the interplay between social and topical structure[C]//Proceedings of the 7th International AAAI Conference on Weblogs and Social Media. USA: AAAI, 2013: 516-525.
[16] Weng L, Menczer F, Ahn Y Y. Predicting successful memes using network and community structure[C]//Proceedings of the 8th International AAAI Conference on Weblogs and Social Media. USA: AAAI, 2014: 525-544.
[17] Martin T, Hofman J M, Sharma A,et al. Exploring limits to prediction in complex social systems[C]//Proceedings of the 25th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee,2016: 683-694.
[18] Cao Q, Shen H, Gao H,et al. Predicting the popularity of online content with group-specific models[C]//Proceedings of the 26th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee,2017: 765-766.
[19] Bao P, Shen H W, Huang J,et al. Popularity prediction in microblogging network: A case study on Sina Weibo[C]//Proceedings of the 22nd International Conference on World Wide Web. USA: ACM, 2013: 177-178.
[20] Szabo G, Huberman B A. Predicting the popularity of online content[J]. Communications of the ACM, 2010,53(8): 80-88.
[21] Li C, Ma J, Guo X,et al. Deepcas: An end-to-end predictor of information cascades[C]//Proceedings of the 26th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee,2017: 577-586.
[22] Leskovec J, Kleinberg J, Faloutsos C. Graphs over time: Densification laws, shrinking diameters and possible explanations[C]//Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining.USA: ACM, 2005: 177-187.
[23] Chen X, Zhou F, Zhang K,et al. Information diffusion prediction via recurrent cascades convolution[C]//Proceedings of the 35th International Conference on Data Engineering. USA: IEEE, 2019: 770-781.
[24] Zhang J, Liu B, Tang J,et al. Social influence locality for modeling retweeting behaviors[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence. USA: AAAI, 2013: 2761-2767.
[25] Qiu J, Tang J, Ma H,et al. DeepInf: Social influence prediction with deep learning[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2018: 2110-2119.
[26] Bakshy E, Hofman J M, Mason W A,et al. Everyone's an influencer: Quantifying influence on Twitter[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining. USA: ACM, 2011: 65-74.
[27] Ahmed M, Spagna S, Huici F,et al. A peek into the future: Predicting the evolution of popularity in user generated content[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining. USA: ACM, 2013: 607-616.
[28] Tsur O, Rappoport A. What's in a hashtag: Content based prediction of the spread of ideas in microblogging communities[C]//Proceedings of the 5th ACM International Conference on Web Search and Data Mining. USA: ACM, 2012: 643-652.
[29] Petrovic S, Osborne M, Lavrenko V. Rt to win!Predicting message propagation in Twitter[C]// Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. USA: AAAI, 2011: 586-589.
[30] Jenders M, Kasneci G, Naumann F. Analyzing and predicting viral tweets[C]//Proceedings of the 22nd International Conference on World Wide Web. USA: ACM, 2013: 657-664.
[31] 朱海龙, 云晓春, 韩志帅. 基于传播加速度的微博流行度预测方法[J]. 计算机研究与发展, 2018, 55(6): 1282-1293.
[32] 高金华, 沈华伟, 程学旗, 等. 基于相似消息的流行度预测方法[J]. 中文信息学报, 2018, 32(11): 79-85.
[33] Gao S, Ma J, Chen Z. Effective and effortless features for popularity prediction in microblogging network[C]//Proceedings of the 23rd International Conference on World Wide Web. USA: ACM, 2014: 269-270.
[34] 曹玖新, 吴江林, 石伟, 等. 新浪微博网信息传播分析与预测[J]. 计算机学报, 2014, 37(4): 779-790.
[35] Gao S, Ma J, Chen Z. Modeling and predicting retweeting dynamics on microblogging platforms[C]//Proceedings of the 8th ACM International Conference on Web Search and Data Mining. USA: ACM, 2015: 107-116.
[36] Bao P, Shen H W, Jin X,et al. Modeling and predicting popularity dynamics of microblogs using self-excited hawkes processes[C]//Proceedings of the 24th International Conference on World Wide Web. USA: ACM, 2015: 9-10.
[37] Mishra S, Rizoiu M A, Xie L. Feature driven and point process approaches for popularity prediction[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management. USA: ACM, 2016: 1069-1078.
[38] Yu L, Cui P, Wang F,et al. From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics[C]//Proceedings of the 2015 IEEE International Conference on Data Mining. USA: IEEE, 2015: 559-568.
[39] Du N, Dai H, Trivedi R,et al. Recurrent marked temporal point processes: Embedding event history to vector[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2016: 1555-1564.
[40] Xiao S, Yan J, Yang X, et al. Modeling the intensity function of point process via recurrent neural networks[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. USA: AAAI, 2017: 1597-1603.
[41] Wang J, Zheng V W, Liu Z, et al. Topological recurrent neural network for diffusion pre-diction[C]//Proceedings of the 17th IEEE International Conference on Data Mining. USA: IEEE, 2017: 475-484.
[42] Wu B, Cheng W H, Zhang Y, et al. Sequential prediction of social media popularity with deep temporal context networks[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. USA: AAAI, 2017: 3062-3068.
[43] Xiao S, Farajtabar M, Ye X, et al. Wasserstein learning of deep generative point process models[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. USA: Curran Associates, 2017: 3247-3257.
[44] Chen G, Kong Q, Mao W. An attention-based neural popularity prediction model for social media events[C]//Proceedings of the 2017 IEEE International Conference on Intelligence and Security Informatics. USA: IEEE, 2017: 161-163.
[45] Xiao S, Xu H, Yan J, et al. Learning conditional generative models for temporal point processes[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. USA: AAAI, 2018: 6302-6309.
[46] Mishra S, Rizoiu M A, Xie L. Modeling popularity in asynchronous social media streams with recurrent neural networks[C]//Proceedings of the 20th International AAAI Conference on Web and Social Media. USA: AAAI, 2018: 201-210.
[47] Wang W, Zhang W, Wang J, et al. Learning sequential correlation for user generated textual content popularity prediction[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. USA: AAAI, 2018: 1625-1631
[48] Yan J, Liu X, Shi L, et al. Improving maximum likelihood estimation of temporal point process via discriminative and adversarial learning[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. USA: AAAI, 2018: 2948-2954.
[49] Dou H, Zhao W X, Zhao Y, et al. Predicting the popularity of online content with knowledge-enhanced neural networks[C]//Proceedings of the 24th KDD Deep Learning Day. 2018.
[50] Wu Q, Yang C, Zhang H, et al. Adversarial training model unifying feature driven and point process perspectives for event popularity prediction[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. USA: ACM, 2018: 517-526.
[51] Li S, Xiao S, Zhu S, et al. Learning temporal point processes via reinforcement learning[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. USA: Curran Associates, 2018: 10781-10791.
[52] Upadhyay U, De A, Rodriguez M G. Deep reinforcement learning of marked temporal point processes[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. USA: Curran Associates, 2018: 3168-3178.
[53] Xiao S, Yan J, Yang X, et al. Publication popularity modeling via adversarial learning of profile-specific dynamic process[J]. IEEE Access, 2018, 6: 19984-19992.
[54] Liao D, Xu J, Li G, et al. Popularity prediction on online articles with deep fusion of temporal process and content features[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence. USA: AAAI, 2019: 200-207.
[55] Shao J, Shen H, Cao Q, et al. Temporal convolutional networks for popularity prediction of messages on social medias[C]//Proceedings of the 25th China Conference on Information Retrieval. Germany: Springer, 2019: 135-147.
[56] Zhao W X, Dou H, Zhao Y, et al. Neural network based popularity prediction by linking online content with knowledge bases[C]//Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 2019: 16-28.
[57] Yang C, Tang J, Sun M, et al. Multi-scale information diffusion prediction with reinforced recurrent networks[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. USA: AAAI, 2019: 4033-4039.
[58] Chen X, Zhang K, Zhou F, et al. Information cascades modeling via deep multi-task learning[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. USA: ACM, 2019: 885-888.
[59] Chen G, Kong Q, Xu N, et al. NPP: A neural popularity prediction model for social media content[J]. Neurocomputing, 2019, 14(333): 221-230.
[60] Cao Q, Shen H, Gao J, et al. Popularity prediction on social platforms with coupled graph neural networks [C]//Proceedings of the 13th ACM International Conference on Web Search and Data Mining. USA: ACM, 2020: 70-78.
[61] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of 27th International Conference on Neural Information Processing Systems. USA: Curran Associates, 2013: 3111-3119.
[62] Yang Z, Yang D, Dyer C, et al. Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. USA: Association for Computational Linguistics, 2016: 1480-1489.
[63] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. USA: Curran Associates, 2017: 5998-6008.
[64] Perozzi B, Al-Rfou R, Skiena. S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2014: 701-710.
[65] Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. Switzerland: International World Wide Web Conferences Steering Committee, 2015: 1067-1077.
[66] Zhang J, Tang J, Zhong Y, et al. Structinf: Mining structural influence from social streams[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. USA: AAAI, 2017: 73-79.
[67] Tang J, Zhang J, Yao L, et al. Arnetminer: Extraction and mining of academic social networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2008: 990-998.
[68] Schedl M. The LFM-1b dataset for music retrieval and recommendation[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. USA: ACM, 2016: 103-110.
[69] Harper F M, Konstan J A. The movielens datasets: History and context[J]. ACM Transactions on Interactive Intelligent Systems, 2016, 5(4): 1-19.
[70] Leskovec J, Backstrom L, Kleinberg J. Meme-tracking and the dynamics of the news cycle[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2009: 497-506.
[71] Hodas N O, Lerman K. The simple rules of social contagion[J]. Scientific Reports, 2014, 4(4343): 1-17.
[72] Zhong E, Fan W, Wang J, et al. Comsoc: Adaptive transfer of user behaviors over composite social network[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM, 2012: 696-704.
[73] Kim D, Cho D, Kweon I S. Self-supervised video representation learning with space-time cubic puzzles[C]//Proceedings of the AAAI Conference on Artificial Intelligence. USA: AAAI, 2019: 8545-8552.
[74] Liu Y, Safavi T, Dighe A, et al. Graph summarization methods and applications: A survey[J]. ACM Computing Surveys, 2018, 51(3): 1-34.

基金

国家自然科学基金(91746301, 61472400, 61425016,62002347)
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