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

  Article  MathSciNet  Google Scholar 

• Bach N, Hai N, Phuong T (2016) Personalized recommendation of stories for commenting in forum-based social media. Inf Sci 352–353:48–60

  Article  Google Scholar 

• 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

  Article  MATH  Google Scholar 

• Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont

  MATH  Google Scholar 

• 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

  Article  MathSciNet  MATH  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  MATH  Google Scholar 

• Domenico MD, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980

  Article  Google Scholar 

• Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge

  Book  MATH  Google Scholar 

• Edelmann N (2013) Reviewing the definitions of “lurkers” and some implications for online research. Cyberpsychol Behav Soc Netw 16(9):645–649

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  MathSciNet  MATH  Google Scholar 

• 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

  Article  MathSciNet  MATH  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Book  Google Scholar 

• Lei C, Liu D, Li W (2016) Social diffusion analysis with common-interest model for image annotation. IEEE Trans Multimed 18(4):687–701

  Article  Google Scholar 

• Liu T (2011) Learning to rank for information retrieval. Springer, Berlin

  Book  MATH  Google Scholar 

• Lu H, Chen C, Kong M, Zhang H, Zhao Z (2016) Social recommendation via multi-view user preference learning. Neurocomputing 216:61–71

  Article  Google Scholar 

• Luo Z, Osborne M, Wang T (2015) An effective approach to tweets opinion retrieval. World Wide Web 18(3):545–566

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Google Scholar 

• Tagarelli A, Interdonato R (2015) Time-aware analysis and ranking of lurkers in social networks. Soc Netw Anal Min 5(1):23

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

• 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

  Article  Google Scholar 

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