Question routing via activity-weighted modularity-enhanced factorization
Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and th...
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| Published in | Social network analysis and mining Vol. 12; no. 1; p. 155 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Vienna
Springer Vienna
01.12.2022
Springer Nature B.V |
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| Abstract | Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: (1) sparsity of users’ past records results in lack of personalized recommendation that at times does not match users’ interest or domain expertise, (2) modeling based on all questions and answers content makes periodic updates computationally expensive, and (3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users’ activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models. |
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| AbstractList | Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: (1) sparsity of users’ past records results in lack of personalized recommendation that at times does not match users’ interest or domain expertise, (2) modeling based on all questions and answers content makes periodic updates computationally expensive, and (3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users’ activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models. |
| ArticleNumber | 155 |
| Author | Antulov-Fantulin, Nino Krishna, Vaibhav Vasiliauskaite, Vaiva |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0297627 crossref_primary_10_1145_3690380 crossref_primary_10_1016_j_ipm_2024_103773 crossref_primary_10_1016_j_eswa_2023_121576 crossref_primary_10_1016_j_ins_2024_121116 crossref_primary_10_1109_ACCESS_2024_3450544 |
| Cites_doi | 10.1109/MC.2009.263 10.1007/s13278-020-0626-2 10.1007/s10462-018-09680-6 10.1177/0165551511423149 10.1145/2505515.2505670 10.1145/2766462.2767840 10.1145/170036.170072 10.1609/icwsm.v6i1.14262 10.1145/2396761.2398493 10.1103/PhysRevE.70.066111 10.1145/963770.963775 10.1609/aaai.v33i01.3301192 10.1007/s41109-019-0165-9 10.1145/3331184.3331303 10.1038/s41598-019-41695-z 10.1103/PhysRevE.70.025101 10.1016/j.chb.2016.11.010 10.1007/s10462-015-9443-9 10.1103/PhysRevE.69.066133 10.1109/ASWEC.2015.28 10.1145/2063576.2063885 10.1145/2505515.2505720 10.1145/2934687 10.21105/joss.02174 10.1145/2187980.2188202 10.1088/1742-5468/2008/10/P10008 10.1145/2492517.2492559 10.1145/2187980.2188201 10.1609/icwsm.v7i1.14387 10.1109/SNPD.2019.8935747 10.1007/s11390-018-1845-0 10.1609/icwsm.v3i1.13937 10.1002/9780471462422.eoct979 10.1007/s13278-018-0534-x 10.1103/PhysRevE.77.046119 10.1145/1242572.1242603 10.3758/BF03213979 10.1016/j.csda.2017.10.006 10.1145/324133.324140 10.1007/978-3-662-49390-8_3 10.1109/TKDE.2014.2356461 10.1145/1871437.1871658 10.1016/j.jmva.2006.11.013 10.1016/j.neucom.2018.01.034 10.1016/j.ipm.2017.04.002 10.1145/1148170.1148212 10.1002/widm.1102 10.1109/CyberC.2015.87 10.1109/MS.2016.34 10.1109/ASONAM.2014.6921702 10.1007/978-0-387-85820-3_8 10.1109/ASONAM.2016.7752346 10.1145/1052934.1052942 10.1145/1774088.1774266 |
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| References | Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media vol 3, pp 361–362 Al-TaieMZKadrySObasaAIUnderstanding expert finding systems: domains and techniquesSoc Netw Anal Min2018811910.1007/s13278-018-0534-x YangJBozzonAHoubenG-JE-wise: an expertise-driven recommendation platform for web question answering systemsInternational conference on web engineering2015BerlinSpringer691694 Zhou G, Lai S, Liu K, Zhao J (2012) Topic-sensitive probabilistic model for expert finding in question answer communities. In: Proceedings of the 21st ACM international conference on information and knowledge management, pp 1662–1666 CarissimoACutilloLDe FeisIValidation of community robustnessComput Stat Data Anal2018120124374220510.1016/j.csda.2017.10.0061469.62029 ZhaoZZhangLHeXNgWExpert finding for question answering via graph regularized matrix completionIEEE Trans Knowl Data Eng2014274993100410.1109/TKDE.2014.2356461 Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, pp 207–216 TraagVAWaltmanLVan EckNJFrom louvain to leiden: guaranteeing well-connected communitiesSci Rep20199111210.1038/s41598-019-41695-z Sung J, Lee J-G, Lee U (2013) Booming up the long tails: discovering potentially contributive users in community-based question answering services. In: Proceedings of the international AAAI conference on web and social media, vol 7, pp 602–610 MeilăMComparing clusterings-an information based distanceJ Multivar Anal2007985873895232541210.1016/j.jmva.2006.11.0131298.91124 WangXHuangCYaoLBenatallahBDongMA survey on expert recommendation in community question answeringJ Comput Sci Technol201833462565310.1007/s11390-018-1845-0 ZhengXHuZXuAChenDLiuKLiBAlgorithm for recommending answer providers in community-based question answeringJ Inf Sci201238131410.1177/0165551511423149 Zhang J, Ackerman MS, Adamic L (2007) Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th international conference on world wide web, pp 221–230 Fukui K, Miyazaki T, Ohira M (2019) Suggesting questions that match each user’s expertise in community question and answering services. In: 2019 20th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), pp 501–506. IEEE HuangZChenHZengDApplying associative retrieval techniques to alleviate the sparsity problem in collaborative filteringACM Trans Inf Syst (TOIS)200422111614210.1145/963770.963775 KorenYBellRVolinskyCMatrix factorization techniques for recommender systemsComputer2009428303710.1109/MC.2009.263 Le LT, Shah C (2016) Retrieving rising stars in focused community question-answering. In: Asian conference on intelligent information and database systems, pp 25–36. Springer, Berlin HugNSurprise: a python library for recommender systemsJ Open Source Softw2020552217410.21105/joss.02174 NajafabadiMKMahrinMNChupratSSarkanHMImproving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit dataComput Hum Behav20176711312810.1016/j.chb.2016.11.010 KarrerBLevinaENewmanMERobustness of community structure in networksPhys Rev E200877404611910.1103/PhysRevE.77.046119 BorodinARobertsGORosenthalJSTsaparasPLink analysis ranking: algorithms, theory, and experimentsACM Trans Int Technol (TOIT)20055123129710.1145/1052934.1052942 Ji Z, Wang B (2013) Learning to rank for question routing in community question answering. In: Proceedings of the 22nd ACM international conference on information and knowledge management, pp 2363–2368 GuimeraRSales-PardoMAmaralLANModularity from fluctuations in random graphs and complex networksPhys Rev E200470202510110.1103/PhysRevE.70.025101 ShaniGGunawardanaAEvaluating recommendation systemsRecomm Syst Handb2011BostonSpringer25729710.1007/978-0-387-85820-3_8 RossettiGMilliLCazabetRCdlib: a python library to extract, compare and evaluate communities from complex networksAppl Netw Sci20194112610.1007/s41109-019-0165-9 Chang S, Pal A (2013) Routing questions for collaborative answering in community question answering. In: 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2013), pp 494–501. IEEE Kao W-C, Liu D-R, Wang S-W (2010) Expert finding in question-answering websites: a novel hybrid approach. In: Proceedings of the 2010 ACM symposium on applied computing, pp 867–871 Sontag D, Roy D (2011) Complexity of inference in latent dirichlet allocation. Advances in neural information processing systems, p 24 SrbaIBielikovaMWhy is stack overflow failing? preserving sustainability in community question answeringIEEE Softw2016334808910.1109/MS.2016.34 van Dijk D, Tsagkias M, de Rijke M (2015) Early detection of topical expertise in community question answering. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 995–998 Pal A, Chang S, Konstan J (2012) Evolution of experts in question answering communities. In: Proceedings of the international AAAI conference on web and social media, vol 6, pp 274–281 Li Z, Jiang J-Y, Sun Y, Wang W (2019) Personalized question routing via heterogeneous network embedding. In: Proceedings of the AAAI conference on artificial intelligence vol 33, pp 192–199 IdrissiNZellouAA systematic literature review of sparsity issues in recommender systemsSoc Netw Anal Min202010112310.1007/s13278-020-0626-2 Woolson RF (2007) Wilcoxon signed‐rank test. Wiley encyclopedia of clinical trials, pp 1–3 MomtaziSNaumannFTopic modeling for expert finding using latent dirichlet allocationWiley Interdiscip Rev Data Min Knowl Discov20133534635310.1002/widm.1102 Li B, King I, Lyu MR (2011) Question routing in community question answering: putting category in its place. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 2041–2044 KleinbergJMAuthoritative sources in a hyperlinked environmentJ ACM (JACM)1999465604632174764910.1145/324133.3241401065.68660 Zhou TC, Lyu MR, King I (2012) A classification-based approach to question routing in community question answering. In: Proceedings of the 21st international conference on world wide web, pp 783–790 Bishop, Christopher M (2006) Pattern recognition and machine learning. New York: Springer BleiDMNgAYJordanMILatent dirichlet allocationJ Mach Learn Res20033Jan99310221112.68379 ChenZZhangCZhaoZYaoCCaiDQuestion retrieval for community-based question answering via heterogeneous social influential networkNeurocomputing201828511712410.1016/j.neucom.2018.01.034 NajafabadiMKMahrinMNA systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedbackArtif Intell Rev201645216720110.1007/s10462-015-9443-9 Choetkiertikul M, Avery D, Dam HK, Tran T, Ghose A (2015) Who will answer my question on stack overflow? In: 2015 24th Australasian software engineering conference, pp 155–164. IEEE Yang B, Manandhar S (2014) Tag-based expert recommendation in community question answering. In: 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2014), pp 960–963. IEEE SrbaIBielikovaMA comprehensive survey and classification of approaches for community question answeringACM Trans Web (TWEB)201610316310.1145/2934687 BlondelVDGuillaumeJ-LLambiotteRLefebvreEFast unfolding of communities in large networksJ Stat Mech Theory Exp20082008101000810.1088/1742-5468/2008/10/P100081459.91130 YuanSZhangYTangJHallWCabotàJBExpert finding in community question answering: a reviewArtif Intell Rev202053284387410.1007/s10462-018-09680-6 Jeon J, Croft WB, Lee JH, Park S (2006) A framework to predict the quality of answers with non-textual features. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, pp 228–235 ClausetANewmanMEMooreCFinding community structure in very large networksPhys Rev E200470606611110.1103/PhysRevE.70.066111 Pal A, Konstan JA (2010) Expert identification in community question answering: exploring question selection bias. In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1505–1508 Wang L, Wu B, Yang J, Peng S (2016) Personalized recommendation for new questions in community question answering. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 901–908. IEEE PageLBrinSMotwaniRWinogradTThe pagerank citation ranking: bringing order to the web1999Stanford InfoLabTechnical report Yang L, Qiu M, Gottipati S, Zhu F, Jiang J, Sun H, Chen Z (2013) Cqarank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM international conference on information and knowledge management, pp 99–108 GreenLFristoeNMyersonJTemporal discounting and preference reversals in choice between delayed outcomesPsychon Bull Rev19941338338910.3758/BF03213979 Surowiecki J (2005) The wisdom of crowds NewmanMEFast algorithm for detecting community structure in networksPhys Rev E200469606613310.1103/PhysRevE.69.066133 Riahi F, Zolaktaf Z, Shafiei M, Milios E (2012) Finding expert users in community question answering. In: Proceedings of the 21st international conference on world wide web, pp 791–798 Li H, Jin S, Shudong L (2015) A hybrid model for experts finding in community question answering. In: 2015 International conference on cyber-enabled distributed computing and knowledge discovery, pp 176–185. IEEE NeshatiMFallahnejadZBeigyHOn dynamicity of expert finding in community question answeringInf Process Manage20175351026104210.1016/j.ipm.2017.04.002 Dai Z, Callan J (2019) Deeper text understanding for ir with contextual neural language modeling. In: Pr X Wang (978_CR49) 2018; 33 A Borodin (978_CR7) 2005; 5 S Momtazi (978_CR31) 2013; 3 978_CR50 MK Najafabadi (978_CR32) 2016; 45 978_CR54 978_CR53 VD Blondel (978_CR6) 2008; 2008 978_CR51 978_CR14 978_CR13 978_CR56 978_CR11 G Shani (978_CR41) 2011 S Yuan (978_CR55) 2020; 53 978_CR9 978_CR59 978_CR4 JM Kleinberg (978_CR24) 1999; 46 ME Newman (978_CR35) 2004; 69 M Meilă (978_CR30) 2007; 98 Z Zhao (978_CR57) 2014; 27 A Clauset (978_CR12) 2004; 70 VA Traag (978_CR47) 2019; 9 978_CR60 A Carissimo (978_CR8) 2018; 120 978_CR21 978_CR20 MK Najafabadi (978_CR33) 2017; 67 978_CR22 978_CR29 978_CR28 978_CR27 978_CR26 J Yang (978_CR52) 2015 I Srba (978_CR44) 2016; 33 L Page (978_CR36) 1999 DM Blei (978_CR5) 2003; 3 M Neshati (978_CR34) 2017; 53 978_CR39 978_CR38 978_CR37 MZ Al-Taie (978_CR2) 2018; 8 G Rossetti (978_CR40) 2019; 4 978_CR3 978_CR1 X Zheng (978_CR58) 2012; 38 N Hug (978_CR18) 2020; 5 B Karrer (978_CR23) 2008; 77 Y Koren (978_CR25) 2009; 42 Z Huang (978_CR17) 2004; 22 978_CR42 I Srba (978_CR43) 2016; 10 978_CR46 978_CR45 R Guimera (978_CR16) 2004; 70 N Idrissi (978_CR19) 2020; 10 978_CR48 L Green (978_CR15) 1994; 1 Z Chen (978_CR10) 2018; 285 |
| References_xml | – reference: Fukui K, Miyazaki T, Ohira M (2019) Suggesting questions that match each user’s expertise in community question and answering services. In: 2019 20th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), pp 501–506. IEEE – reference: KorenYBellRVolinskyCMatrix factorization techniques for recommender systemsComputer2009428303710.1109/MC.2009.263 – reference: Li H, Jin S, Shudong L (2015) A hybrid model for experts finding in community question answering. In: 2015 International conference on cyber-enabled distributed computing and knowledge discovery, pp 176–185. IEEE – reference: Jeon J, Croft WB, Lee JH, Park S (2006) A framework to predict the quality of answers with non-textual features. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, pp 228–235 – reference: ChenZZhangCZhaoZYaoCCaiDQuestion retrieval for community-based question answering via heterogeneous social influential networkNeurocomputing201828511712410.1016/j.neucom.2018.01.034 – reference: Ji Z, Wang B (2013) Learning to rank for question routing in community question answering. In: Proceedings of the 22nd ACM international conference on information and knowledge management, pp 2363–2368 – reference: KarrerBLevinaENewmanMERobustness of community structure in networksPhys Rev E200877404611910.1103/PhysRevE.77.046119 – reference: Li Z, Jiang J-Y, Sun Y, Wang W (2019) Personalized question routing via heterogeneous network embedding. In: Proceedings of the AAAI conference on artificial intelligence vol 33, pp 192–199 – reference: MomtaziSNaumannFTopic modeling for expert finding using latent dirichlet allocationWiley Interdiscip Rev Data Min Knowl Discov20133534635310.1002/widm.1102 – reference: Chang S, Pal A (2013) Routing questions for collaborative answering in community question answering. In: 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2013), pp 494–501. IEEE – reference: CarissimoACutilloLDe FeisIValidation of community robustnessComput Stat Data Anal2018120124374220510.1016/j.csda.2017.10.0061469.62029 – reference: HugNSurprise: a python library for recommender systemsJ Open Source Softw2020552217410.21105/joss.02174 – reference: Choetkiertikul M, Avery D, Dam HK, Tran T, Ghose A (2015) Who will answer my question on stack overflow? In: 2015 24th Australasian software engineering conference, pp 155–164. IEEE – reference: YangJBozzonAHoubenG-JE-wise: an expertise-driven recommendation platform for web question answering systemsInternational conference on web engineering2015BerlinSpringer691694 – reference: Zhou G, Lai S, Liu K, Zhao J (2012) Topic-sensitive probabilistic model for expert finding in question answer communities. In: Proceedings of the 21st ACM international conference on information and knowledge management, pp 1662–1666 – reference: Kao W-C, Liu D-R, Wang S-W (2010) Expert finding in question-answering websites: a novel hybrid approach. In: Proceedings of the 2010 ACM symposium on applied computing, pp 867–871 – reference: NajafabadiMKMahrinMNA systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedbackArtif Intell Rev201645216720110.1007/s10462-015-9443-9 – reference: Yang L, Qiu M, Gottipati S, Zhu F, Jiang J, Sun H, Chen Z (2013) Cqarank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM international conference on information and knowledge management, pp 99–108 – reference: Pal A, Konstan JA (2010) Expert identification in community question answering: exploring question selection bias. In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1505–1508 – reference: ShaniGGunawardanaAEvaluating recommendation systemsRecomm Syst Handb2011BostonSpringer25729710.1007/978-0-387-85820-3_8 – reference: Zhang J, Ackerman MS, Adamic L (2007) Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th international conference on world wide web, pp 221–230 – reference: WangXHuangCYaoLBenatallahBDongMA survey on expert recommendation in community question answeringJ Comput Sci Technol201833462565310.1007/s11390-018-1845-0 – reference: YuanSZhangYTangJHallWCabotàJBExpert finding in community question answering: a reviewArtif Intell Rev202053284387410.1007/s10462-018-09680-6 – reference: Dai Z, Callan J (2019) Deeper text understanding for ir with contextual neural language modeling. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 985–988 – reference: Riahi F, Zolaktaf Z, Shafiei M, Milios E (2012) Finding expert users in community question answering. In: Proceedings of the 21st international conference on world wide web, pp 791–798 – reference: NeshatiMFallahnejadZBeigyHOn dynamicity of expert finding in community question answeringInf Process Manage20175351026104210.1016/j.ipm.2017.04.002 – reference: Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media vol 3, pp 361–362 – reference: HuangZChenHZengDApplying associative retrieval techniques to alleviate the sparsity problem in collaborative filteringACM Trans Inf Syst (TOIS)200422111614210.1145/963770.963775 – reference: ZhengXHuZXuAChenDLiuKLiBAlgorithm for recommending answer providers in community-based question answeringJ Inf Sci201238131410.1177/0165551511423149 – reference: Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, pp 207–216 – reference: Surowiecki J (2005) The wisdom of crowds – reference: RossettiGMilliLCazabetRCdlib: a python library to extract, compare and evaluate communities from complex networksAppl Netw Sci20194112610.1007/s41109-019-0165-9 – reference: Al-TaieMZKadrySObasaAIUnderstanding expert finding systems: domains and techniquesSoc Netw Anal Min2018811910.1007/s13278-018-0534-x – reference: PageLBrinSMotwaniRWinogradTThe pagerank citation ranking: bringing order to the web1999Stanford InfoLabTechnical report – reference: TraagVAWaltmanLVan EckNJFrom louvain to leiden: guaranteeing well-connected communitiesSci Rep20199111210.1038/s41598-019-41695-z – reference: BorodinARobertsGORosenthalJSTsaparasPLink analysis ranking: algorithms, theory, and experimentsACM Trans Int Technol (TOIT)20055123129710.1145/1052934.1052942 – reference: Le LT, Shah C (2016) Retrieving rising stars in focused community question-answering. In: Asian conference on intelligent information and database systems, pp 25–36. Springer, Berlin – reference: ClausetANewmanMEMooreCFinding community structure in very large networksPhys Rev E200470606611110.1103/PhysRevE.70.066111 – reference: NewmanMEFast algorithm for detecting community structure in networksPhys Rev E200469606613310.1103/PhysRevE.69.066133 – reference: Yang B, Manandhar S (2014) Tag-based expert recommendation in community question answering. In: 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2014), pp 960–963. IEEE – reference: Bishop, Christopher M (2006) Pattern recognition and machine learning. New York: Springer – reference: Zhou TC, Lyu MR, King I (2012) A classification-based approach to question routing in community question answering. In: Proceedings of the 21st international conference on world wide web, pp 783–790 – reference: ZhaoZZhangLHeXNgWExpert finding for question answering via graph regularized matrix completionIEEE Trans Knowl Data Eng2014274993100410.1109/TKDE.2014.2356461 – reference: MeilăMComparing clusterings-an information based distanceJ Multivar Anal2007985873895232541210.1016/j.jmva.2006.11.0131298.91124 – reference: GreenLFristoeNMyersonJTemporal discounting and preference reversals in choice between delayed outcomesPsychon Bull Rev19941338338910.3758/BF03213979 – reference: KleinbergJMAuthoritative sources in a hyperlinked environmentJ ACM (JACM)1999465604632174764910.1145/324133.3241401065.68660 – reference: Li B, King I, Lyu MR (2011) Question routing in community question answering: putting category in its place. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 2041–2044 – reference: NajafabadiMKMahrinMNChupratSSarkanHMImproving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit dataComput Hum Behav20176711312810.1016/j.chb.2016.11.010 – reference: GuimeraRSales-PardoMAmaralLANModularity from fluctuations in random graphs and complex networksPhys Rev E200470202510110.1103/PhysRevE.70.025101 – reference: SrbaIBielikovaMA comprehensive survey and classification of approaches for community question answeringACM Trans Web (TWEB)201610316310.1145/2934687 – reference: van Dijk D, Tsagkias M, de Rijke M (2015) Early detection of topical expertise in community question answering. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 995–998 – reference: Wang L, Wu B, Yang J, Peng S (2016) Personalized recommendation for new questions in community question answering. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 901–908. IEEE – reference: Woolson RF (2007) Wilcoxon signed‐rank test. Wiley encyclopedia of clinical trials, pp 1–3 – reference: Sontag D, Roy D (2011) Complexity of inference in latent dirichlet allocation. Advances in neural information processing systems, p 24 – reference: BleiDMNgAYJordanMILatent dirichlet allocationJ Mach Learn Res20033Jan99310221112.68379 – reference: BlondelVDGuillaumeJ-LLambiotteRLefebvreEFast unfolding of communities in large networksJ Stat Mech Theory Exp20082008101000810.1088/1742-5468/2008/10/P100081459.91130 – reference: IdrissiNZellouAA systematic literature review of sparsity issues in recommender systemsSoc Netw Anal Min202010112310.1007/s13278-020-0626-2 – reference: Sung J, Lee J-G, Lee U (2013) Booming up the long tails: discovering potentially contributive users in community-based question answering services. In: Proceedings of the international AAAI conference on web and social media, vol 7, pp 602–610 – reference: Pal A, Chang S, Konstan J (2012) Evolution of experts in question answering communities. In: Proceedings of the international AAAI conference on web and social media, vol 6, pp 274–281 – reference: SrbaIBielikovaMWhy is stack overflow failing? preserving sustainability in community question answeringIEEE Softw2016334808910.1109/MS.2016.34 – volume: 42 start-page: 30 issue: 8 year: 2009 ident: 978_CR25 publication-title: Computer doi: 10.1109/MC.2009.263 – volume: 10 start-page: 1 issue: 1 year: 2020 ident: 978_CR19 publication-title: Soc Netw Anal Min doi: 10.1007/s13278-020-0626-2 – volume: 53 start-page: 843 issue: 2 year: 2020 ident: 978_CR55 publication-title: Artif Intell Rev doi: 10.1007/s10462-018-09680-6 – volume: 38 start-page: 3 issue: 1 year: 2012 ident: 978_CR58 publication-title: J Inf Sci doi: 10.1177/0165551511423149 – ident: 978_CR21 doi: 10.1145/2505515.2505670 – volume-title: The pagerank citation ranking: bringing order to the web year: 1999 ident: 978_CR36 – ident: 978_CR48 doi: 10.1145/2766462.2767840 – ident: 978_CR1 doi: 10.1145/170036.170072 – ident: 978_CR37 doi: 10.1609/icwsm.v6i1.14262 – ident: 978_CR60 doi: 10.1145/2396761.2398493 – volume: 70 start-page: 066111 issue: 6 year: 2004 ident: 978_CR12 publication-title: Phys Rev E doi: 10.1103/PhysRevE.70.066111 – volume: 22 start-page: 116 issue: 1 year: 2004 ident: 978_CR17 publication-title: ACM Trans Inf Syst (TOIS) doi: 10.1145/963770.963775 – ident: 978_CR27 doi: 10.1609/aaai.v33i01.3301192 – volume: 4 start-page: 1 issue: 1 year: 2019 ident: 978_CR40 publication-title: Appl Netw Sci doi: 10.1007/s41109-019-0165-9 – ident: 978_CR4 – volume: 3 start-page: 993 issue: Jan year: 2003 ident: 978_CR5 publication-title: J Mach Learn Res – ident: 978_CR13 doi: 10.1145/3331184.3331303 – volume: 9 start-page: 1 issue: 1 year: 2019 ident: 978_CR47 publication-title: Sci Rep doi: 10.1038/s41598-019-41695-z – volume: 70 start-page: 025101 issue: 2 year: 2004 ident: 978_CR16 publication-title: Phys Rev E doi: 10.1103/PhysRevE.70.025101 – volume: 67 start-page: 113 year: 2017 ident: 978_CR33 publication-title: Comput Hum Behav doi: 10.1016/j.chb.2016.11.010 – volume: 45 start-page: 167 issue: 2 year: 2016 ident: 978_CR32 publication-title: Artif Intell Rev doi: 10.1007/s10462-015-9443-9 – volume: 69 start-page: 066133 issue: 6 year: 2004 ident: 978_CR35 publication-title: Phys Rev E doi: 10.1103/PhysRevE.69.066133 – ident: 978_CR11 doi: 10.1109/ASWEC.2015.28 – ident: 978_CR29 doi: 10.1145/2063576.2063885 – ident: 978_CR54 doi: 10.1145/2505515.2505720 – volume: 10 start-page: 1 issue: 3 year: 2016 ident: 978_CR43 publication-title: ACM Trans Web (TWEB) doi: 10.1145/2934687 – volume: 5 start-page: 2174 issue: 52 year: 2020 ident: 978_CR18 publication-title: J Open Source Softw doi: 10.21105/joss.02174 – ident: 978_CR39 doi: 10.1145/2187980.2188202 – volume: 2008 start-page: 10008 issue: 10 year: 2008 ident: 978_CR6 publication-title: J Stat Mech Theory Exp doi: 10.1088/1742-5468/2008/10/P10008 – ident: 978_CR9 doi: 10.1145/2492517.2492559 – ident: 978_CR59 doi: 10.1145/2187980.2188201 – ident: 978_CR45 doi: 10.1609/icwsm.v7i1.14387 – ident: 978_CR14 doi: 10.1109/SNPD.2019.8935747 – ident: 978_CR42 – volume: 33 start-page: 625 issue: 4 year: 2018 ident: 978_CR49 publication-title: J Comput Sci Technol doi: 10.1007/s11390-018-1845-0 – ident: 978_CR3 doi: 10.1609/icwsm.v3i1.13937 – ident: 978_CR51 doi: 10.1002/9780471462422.eoct979 – volume: 8 start-page: 1 issue: 1 year: 2018 ident: 978_CR2 publication-title: Soc Netw Anal Min doi: 10.1007/s13278-018-0534-x – volume: 77 start-page: 046119 issue: 4 year: 2008 ident: 978_CR23 publication-title: Phys Rev E doi: 10.1103/PhysRevE.77.046119 – ident: 978_CR46 – ident: 978_CR56 doi: 10.1145/1242572.1242603 – volume: 1 start-page: 383 issue: 3 year: 1994 ident: 978_CR15 publication-title: Psychon Bull Rev doi: 10.3758/BF03213979 – volume: 120 start-page: 1 year: 2018 ident: 978_CR8 publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2017.10.006 – volume: 46 start-page: 604 issue: 5 year: 1999 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