Learning to lurker rank: an evaluation of learning-to-rank methods for lurking behavior analysis

Aprem A, Krishnamurthy V (2017) Utility change point detection in online social media: a revealed preference framework. IEEE Trans Signal Process 65(7):1869–1880 Bach N, Hai N, Phuong T (2016) Personalized recommendation of stories for commenting in forum-based social media. Inf Sci 352–353:48–60 Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on Twitter. In: Proc. ACM conf. on web search and web data mining (WSDM), Hong Kong, China, February 9–12, 2011, pp 65–74. https://doi.org/10.1145/1935826.1935845 Basile P, Caputo A, Rossiello G, Semeraro G (2016) Learning to rank entity relatedness through embedding-based features. In: Proc. int. conf. on applications of natural language to information systems (NLDB), Salford, UK, June 22–24, 2016, pp 471–477. https://doi.org/10.1007/978-3-319-41754-7_51 Biega AJ, Ghazimatin A, Ferhatosmanoglu H, Gummadi KP, Weikum G (2017) Learning to Un-Rank: quantifying search exposure for users in online communities. In: Proceedings of the 2017 ACM on conference on information and knowledge management—CIKM ’17, ACM Press, New York, pp 267–276 Breiman L (2001) Random forests. Mach Learn 45(1):5–32 Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont Burges CJ (2010) From ranknet to lambdarank to lambdamart: an overview. Tech. Rep. 23-581 Burges CJC, Ragno R, Le QV (2006) Learning to rank with nonsmooth cost functions. In: Proc. conf. on neural information processing systems (NIPS), MIT Press Cambridge, MA, USA, Canada, December 04–07, 2006, pp 193–200 Burges CJC, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender GN (2005) Learning to rank using gradient descent. In: Proc. int. conf. on machine learning (ICML), Bonn, Germany, August 7–11, 2005, pp 89–96. https://doi.org/10.1145/1102351.1102363 Busa-Fekete R, Szarvas G, Elteto T, Kégl B (2012) An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain. In: Proc. ECAI work. on preference learning: problems and applications in AI, vol 242, Ios Press Chakrabarti S (2007) Learning to rank in vector spaces and social networks. Internet Math 4(1–3):267–298 Chen C, Shih SY, Lee M (2016) Who should you follow? Combining learning to rank with social influence for informative friend recommendation. Decis Support Syst 90:33–45 Chen W, Liu TY, Lan Y, Ma Z, Li H (2009) Ranking measures and loss functions in learning to rank. In: Proc. conf. on neural information processing systems (NIPS), Vancouver, British Columbia, Canada, pp 315–323 Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297 Domenico MD, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980 Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge Edelmann N (2013) Reviewing the definitions of “lurkers” and some implications for online research. Cyberpsychol Behav Soc Netw 16(9):645–649 Farzan R, Brusilovsky P (2011) Encouraging user participation in a course recommender system: an impact on user behavior. Comput Hum Behav 27(1):276–284 Farzan R, DiMicco JM, Brownholtz B (2010) Mobilizing lurkers with a targeted task. In: Proc. int. conf. on weblogs and social media (ICWSM), Washington, DC, USA, May 23–26, 2010 Ferro N, Lucchese C, Maistro M, Perego R (2017) On including the user dynamic in learning to rank. In: Proc. ACM SIGIR conf. on research and development in information retrieval (SIGIR), Shinjuku, Tokyo, Japan, August 7–11, 2017, pp 1041–1044. https://doi.org/10.1145/3077136.3080714 Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proc. European conf. on computational learning theory (EuroCOLT), Barcelona, Spain, March 13–15, 1995, pp 23–37. https://doi.org/10.1007/3-540-59119-2_166 Friedman JH (2000) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232 Hall MA (1999) Correlation-based feature selection for machine learning. PhD Thesis, University of Waikato, Hamilton Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349–360 Hofmann K, Whiteson S, Schuth A, de Rijke M (2014) Learning to rank for information retrieval from user interactions. ACM SIGWEB Newsletter. https://doi.org/10.1145/2591453.2591458 Hong L, Bekkerman R, Adler J, Davison B (2012) Learning to rank social update streams. In: Proc. ACM SIGIR conf. on research and development in information retrieval (SIGIR), Portland, OR, USA, August 12–16, 2012, pp 651–660. https://doi.org/10.1145/2348283.2348371 Huang J, Zhang W, Zhao S, Ding S, Wang H (2017) Learning to explain entity relationships by pairwise ranking with convolutional neural networks. In: Proc. int. joint conf. on artificial intelligence (IJCAI), pp 4018–4025 Imlawi J, Gregg DG (2014) Engagement in online social networks: the impact of self-disclosure and humor. Int J Hum Comput Interact 30(2):106–125 Interdonato R, Pulice C, Tagarelli A (2015) “Got to have faith!”: The DEvOTION algorithm for delurking in social networks. In: Proc. int. conf. on advances in social networks analysis and mining (ASONAM), Paris, France, August 25–28, 2015, pp 314–319. https://doi.org/10.1145/2808797.2809394 Interdonato R, Tagarelli A (2016) To trust or not to trust lurkers?: evaluation of lurking and trustworthiness in ranking problems. In: Proc. int. school and conf. on network science (NetSciX), Poland, January 11–13, 2016. https://doi.org/10.1007/978-3-319-28361-6_4 Järvelin K, Kekäläinen J (2000) IR evaluation methods for retrieving highly relevant documents. In: Proc. ACM SIGIR conf. on research and development in information retrieval (SIGIR), pp 41–48 Javarone MA, Interdonato R, Tagarelli A (2016) Modeling evolutionary dynamics of lurking in social networks. In: Proc. 7th workshop on complex networks (CompleNet), Dijon, France, March 23–25, 2016, pp 227–239. https://doi.org/10.1007/978-3-319-30569-1_17 Lave J, Wenger E (1991) Situated learning: legitimate peripheral participation. Cambridge University Press, Cambridge Lei C, Liu D, Li W (2016) Social diffusion analysis with common-interest model for image annotation. IEEE Trans Multimed 18(4):687–701 Liu T (2011) Learning to rank for information retrieval. Springer, Berlin Lu H, Chen C, Kong M, Zhang H, Zhao Z (2016) Social recommendation via multi-view user preference learning. Neurocomputing 216:61–71 Luo Z, Osborne M, Wang T (2015) An effective approach to tweets opinion retrieval. World Wide Web 18(3):545–566 Malliaros FD, Vazirgiannis M (2013) To stay or not to stay: modeling engagement dynamics in social graphs. In: Proc. ACM conf. on information and knowledge management (CIKM), San Francisco, CA, USA, October 27–November 1, 2013, pp 469–478. https://doi.org/10.1145/2505515.2505561 Martin S, Brown WM, Klavans R, Boyack KW (2011) Openord: an open-source toolbox for large graph layout. In: Visualization and data analysis, San Francisco Airport, CA, USA, January 24–25, 2011, p 786806. https://doi.org/10.1117/12.871402 Martins F, Magalhes J, Callan J (2016) Barbara made the news: mining the behavior of crowds for time-aware learning to rank. In: Proc. ACM conf. on web search and web data mining (WSDM), San Francisco, CA, USA, February 22–25, 2016, pp 667–676. https://doi.org/10.1145/2835776.2835825 Metzler D, Croft WB (2007) Linear feature-based models for information retrieval. Inf Retr 10(3):257–274 Nonnecke B, Preece JJ (2000) Lurker demographics: counting the silent. In: Proc. ACM conf. on human factors in computing systems (CHI), pp 73–80 Pan Z, Lu Y, Gupta S (2014) How heterogeneous community engage newcomers? The effect of community diversity on newcomers’ perception of inclusion: an empirical study in social media service. Comput Hum Behav 39:100–111 Perna D, Interdonato R, Tagarelli A (2018) Identifying users with alternate behaviors of lurking and active participation in multilayer social networks. IEEE Trans Comput Soc Syst 5(1):46–63 Perna D, Tagarelli A (2017) An evaluation of learning-to-rank methods for lurking behavior analysis. In: Proc. int. conf. on user modeling, adaptation and personalization, UMAP, Bratislava, Slovakia, July 09–12, 2017, pp 381–382. https://doi.org/10.1145/3079628.3079650 Preece JJ, Nonnecke B, Andrews D (2004) The top five reasons for lurking: improving community experiences for everyone. Comput Hum Behav 20(2):201–223 Rowe M (2013) Mining user lifecycles from online community platforms and their application to churn prediction. In: Proc. IEEE int. conf. on data mining (ICDM), Dallas, TX, USA, December 7–10, 2013, pp 637–646. https://doi.org/10.1109/ICDM.2013.78 Silvestri G, Yang J, Bozzon A, Tagarelli A (2015) Linking accounts across social networks: the case of stackoverflow, github and twitter. In: Proc. int. workshop on knowledge discovery on the web, Cagliari, Italy, September 3–5, 2015, pp 41–52. http://ceur-ws.org/Vol-1489/paper-05.pdf Srinivasan B, Natarajan A, Sinha R, Gupta V, Revankar S, Ravindran B (2013) Will your Facebook post be engaging? In: Proc. ACM conf. on information and knowledge management (CIKM), pp 25–28 Sun N, Rau PPL, Ma L (2014) Understanding lurkers in online communities: a literature review. Comput Hum Behav 38:110–117 Tabourier L, Libert A, Lambiotte R (2014) RankMerging: learning to rank in large-scale social networks. In: Proc. ECML PKDD workshop on dynamic networks and knowledge discovery (DyNaK), Nancy, France, September 15, 2014 Tagarelli A, Interdonato R (2013) “Who’s out there?”: identifying and ranking lurkers in social networks. In: Proc. int. conf. on advances in social networks analysis and mining (ASONAM), Niagara, ON, Canada, August 25–29, 2013, pp 215–222. https://doi.org/10.1145/2492517.2492542 Tagarelli A, Interdonato R (2014) Lurking in social networks: topology-based analysis and ranking methods. Soc Netw Anal Min 4(230):27 Tagarelli A, Interdonato R (2015) Time-aware analysis and ranking of lurkers in social networks. Soc Netw Anal Min 5(1):23 Tian Q, Li B (2016) Weakly hierarchical lasso based learning to rank in best answer prediction. In: Proc. int. conf. on advances in social networks analysis and mining (ASONAM), San Francisco, CA, USA, August 18–21, 2016, pp 307–314. https://doi.org/10.1109/ASONAM.2016.7752250 Tsai CH, Lou JK, Lu WC, Lin SD (2014) Exploiting rank-learning models to predict the diffusion of preferences on social networks. In: Proc. int. conf. on advances in social networks analysis and mining (ASONAM), Beijing, China, August 17–20, 2014, pp 265–272. https://doi.org/10.1109/ASONAM.2014.6921595 Tsai H, Pai P (2014) Why do newcomers participate in virtual communities? An integration of self-determination and relationship management theories. Decis Support Syst 57:178–187. https://doi.org/10.1016/j.dss.2013.09.001 Wang Y, Wang L, Li Y, He D, Liu T (2013) A theoretical analysis of NDCG type ranking measures. In: Proc. annual conf. on learning theory (COLT), Princeton University, NJ, USA, June 12–14, 2013, pp 25–54 Wu Q, Burges CJC, Svore KM, Gao J (2010) Adapting boosting for information retrieval measures. Inf Retr 13(3):254–270 Xu J, Li H (2007) Adarank: a boosting algorithm for information retrieval. In: Proc. ACM SIGIR conf. on research and development in information retrieval (SIGIR), Amsterdam, The Netherlands, July 23–27, 2007, pp 391–398. https://doi.org/10.1145/1277741.1277809 Xu T, Zhu H, Chen E, Huai B, Xiong H, Tian J (2014) Learning to annotate via social interaction analytics. Knowl Inf Syst 41(2):251–276 Zamani H, Shakery A, Moradi P (2014) Regression and learning to rank aggregation for user engagement evaluation. In: Proc. ACM recommender systems challenge (RecSysChallenge), ACM, p 29 Zhao W, Li S, He Y, Wang L, Wen JR, Li X (2016) Exploring demographic information in social media for product recommendation. Knowl Inf Syst 49(1):61–89 Zhuang J, Mei T, Hoi S, Hua XS, Li S (2011) Modeling social strength in social media community via kernel-based learning. In: Proc. ACM multimedia conf. and co-located workshops, Scottsdale, AZ, USA, November 28–December 1, 2011, pp 113–122. https://doi.org/10.1145/2072298.2072315 Zin T, Tin P, Hama H (2017) Deep learning model for integration of clustering with ranking in social networks. Adv Intell Syst Comput 536:247–254