Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization

• Adomavicius, G., & Kwon, Y. (2014). Optimization-based approaches for maximizing aggregate recomme-ndation diversity. INFORMS Journal on Computing, 26(2), 351–369. https://doi.org/10.1287/ijoc.2013.0570.

• Amrollahi, A. (2019). Burst the filter bubble: Towards an integrated tool. In proceedings of the 30th Australasian conference on information Systems (pp. 12-20). ACIS.

• Amrollahi, A. (2021). A conceptual tool to eliminate filter bubbles in social networks. Australasian Journal of Information Systems, 25, 1–16. https://doi.org/10.3127/ajis.v25i0.2867.

  Article  Google Scholar 

• Ashkan, A., Kveton, B., Berkovsky, S., & Wen, Z. (2015). Optimal greedy diversity for recommendation. In proceeding of the 24th international conference on artificial intelligence (pp. 1742-1748). IJCAI.

• Bag, S., Ghadge, A., & Tiwari, M. K. (2019). An integrated recommender system for improved accuracy and aggregate diversity. Computers & Industrial Engineering, 130, 187–197. https://doi.org/10.1016/j.cie.2019.02.028.

• Barraza-Urbina, A., Heitmann, B., Hayes, C., & Carrillo-Ramos, A. (2015). Xplodiv: An exploitation-expl-oration aware diversification approach for recommender systems. In proceedings of the 28th international Florida artificial intelligence research society conference (pp. 483-488). AAAI.

• Bell, R. M., & Koren, Y. (2007). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In proceedings of the 7th IEEE international conference on data mining (pp. 43-52). IEEE. https://doi.org/10.1109/ICDM.2007.90.

• Bellogín, A., Cantador, I., & Castells, P. (2010). A study of heterogeneity in recommendations for a social music service. In proceedings of the 1st international workshop on information heterogeneity and fusion in recommender Systems (pp. 1-8). HETREC. https://doi.org/10.1145/1869446.1869447.

• Boim, R., Milo, T., & Novgorodov, S. (2011). Diversification and refinement in collaborative filtering rec-ommender. In proceedings of the 20th ACM international conference on information and knowledge management (pp. 739-744). CIKM. https://doi.org/10.1145/2063576.2063684.

• Borodin, A. (2008). Loop-free Markov chains as determinantal point processes. Annales de I'IHP Probabilites et Statistiques, 44(1), 19–28. https://doi.org/10.1214/07-AIHP115.

  Article  Google Scholar 

• Cantador, I., Fernández-Tobías, I., Berkovsky, S., & Cremonesi, P. (2015). Cross-domain recommender systems. In recommender systems handbook (pp. 1-35). Springer. 10.1007/978-0-387-85820-3_1.

• Chakraborty, A., Ali, M., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2017). On quantifying knowledge segregation in society. Social and Information Networks, 2, 1–4. https://doi.org/10.18122/b2sk5h.

  Article  Google Scholar 

• Chandar, P., & Carterette, B. (2013). Preference based evaluation measures for novelty and diversity. In proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval (pp. 413-422). ACM. https://doi.org/10.1145/2484028.2484094.

• Chen, L., Zhang, G., & Zhou, E. (2018). Fast greedy map inference for determinantal point process to improve recommendation diversity. In proceeding of the 32nd conference on neural information processing Systems (pp. 5622-5633). NIPS. https://doi.org/10.5555/3327345.3327465.

• Cheng, P., Wang, S., Ma, J., Sun, J., & Xiong, H. (2017). Learning to recommend accurate and diverse items. In proceedings of the 26th international conference on world wide web (pp. 183-192). ACM. https://doi.org/10.1145/3038912.3052585.

• Chitra, U., & Musco, C. (2020). Analyzing the impact of filter bubbles on social network polarization. In proceedings of the 13th international conference on web search and data mining (pp. 115-123). ACM. https://doi.org/10.1145/3336191.3371825.

• Clarke, C. L., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008). Novelty and diversity in information retrieval evaluation. In proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (pp. 659-666). ACM. https://doi.org/10.1145/1390334.1390446.

• Di Noia, T., Rosati, J., Tomeo, P., & Di Sciascio, E. (2017). Adaptive multi-attribute diversity for recommender systems. Information Sciences, 382, 234–253. https://doi.org/10.1016/j.ins.2016.11.015.

  Article  Google Scholar 

• Dutton, W. H., Reisdorf, B., Dubois, E., & Blank, G. (2017). Social shaping of the politics of internet search and networking: Moving beyond filter bubbles, echo chambers, and fake news. Political Communication, 1–22. https://doi.org/10.1080/10584609.2021.1910887.

• Eady, G., Nagler, J., Guess, A., Zilinsky, J., & Tucker, J. A. (2019). How many people live in political bu- bbles on social media? Evidence from linked survey and twitter data. Sage Open, 9(1), 1–21. https://doi.org/10.1177/2158244019832705.

  Article  Google Scholar 

• Elkahky, A. M., Song, Y., & He, X. (2015). A multi-view deep learning approach for cross domain user modeling in recommendation systems. In proceedings of the 24th international conference on world wide web (pp. 278-288). ACM. https://doi.org/10.1145/2736277.2741667.

• Fernández-Tobías, I., Cantador, I., Tomeo, P., Anelli, V. W., & Di Noia, T. (2019). Addressing the user cold start with cross-domain collaborative filtering: Exploiting item metadata in matrix factorization. User Modeling & User-Adapted Interaction, 29(2), 443–486. https://doi.org/10.1007/s11257-018-9217-6.

  Article  Google Scholar 

• Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320. https://doi.org/10.1093/poq/nfw006.

  Article  Google Scholar 

• Fleder, D., & Hosanagar, K. (2009). Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55(5), 697–712. https://doi.org/10.1287/mnsc.1080.0974.

  Article  Google Scholar 

• Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., & Guo, J. (2013). Cross-domain recommendation via cluster-level latent factor model. In joint European conference on machine learning and knowledge discovery in databases (pp. 161-176). Springer. https://doi.org/10.1007/978-3-642-40991-2_11.

• Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018). Quantifying controversy on social media. ACM Transactions on Social Computing, 1(1), 1–27. https://doi.org/10.7717/peerj-cs.26.

  Article  Google Scholar 

• Ge, Y., Zhao, S., Zhou, H., Pei, C., Sun, F., Ou, W., & Zhang, Y. (2020). Understanding echo chambers in e-commerce recommender systems. In proceedings of the 43rd international ACM SIGIR conference on Research and Development in information retrieval (pp. 2261-2270). ACM. https://doi.org/10.1145/3397271.3401431.

• Gharahighehi, A., & Vens, C. (2021). Diversification in session-based news recommender Systems. Information retrieval, 2, 1-15. Arxiv-2102.03265.

• Gogna, A., & Majumdar, A. (2017). DiABlO: Optimization based design for improving diversity in recommender system. Information Sciences, 378, 59–74. https://doi.org/10.1016/j.ins.2016.10.043.

  Article  Google Scholar 

• Golub, G. H., & Reinsch, C. (1971). Singular value decomposition and least squares solutions. In linear algebra (pp. 134-151). Springer. https://doi.org/10.1007/978-3-662-39778-7_10.

• Guo, G., Zhang, J., & Yorke-Smith, N. (2015). Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In proceedings of the 29th AAAI conference on artificial Intelligence (pp. 123-129). AAAI. https://doi.org/10.5555/2887007.288702510.5555/2887007.2887025.

• Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013). Measuring personalization of web search. In proceedings of the 22nd international conference on world wide web (pp. 527-538). ACM. https://doi.org/10.1145/2488388.2488435.

• Heinrich, B., Hopf, M., Lohninger, D., Schiller, A., & Szubartowicz, M. (2019). Data quality in recommender systems: The impact of completeness of item content data on prediction accuracy of recommender systems. Electronic Markets, 31, 1–21. https://doi.org/10.1007/s12525-019-00366-7.

  Article  Google Scholar 

• Hu, G., Zhang, Y., & Yang, Q. (2018). Conet: Collaborative cross networks for cross-domain recommendation. In proceedings of the 27th ACM international conference on information and knowledge management (pp. 667-676). ACM. https://doi.org/10.1145/3269206.3271684.

• Huang, L., Zhao, Z.-L., Wang, C.-D., Huang, D., & Chao, H.-Y. (2019). LSCD: Low-rank and sparse cross-domain recommendation. Neurocomputing, 366, 86–96. https://doi.org/10.1016/j.neucom.2019.07.091.

  Article  Google Scholar 

• Huang, Y., Zhou, L., Zeng, Z., Duan, L., & Wang, J. (2020). An empirical study on the phenomenon of information narrowing in the context of personalized recommendation. Journal of Physics: Conference Series, 1631(1), 012109. https://doi.org/10.1088/1742-6596/1631/1/012109.

  Article  Google Scholar 

• Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422–446. https://doi.org/10.1145/582415.582418.

  Article  Google Scholar 

• Ji, L. (2020). How to crack the information cocoon room under the background of intelligent media. International Journal of Social Science and Education Research, 3(3), 169–173.

  Google Scholar 

• Jin, Y., Dong, S., Cai, Y., & Hu, J. (2020). RACRec: Review aware cross-domain recommendation for fully-cold-start user. IEEE Access, 8, 55032–55041. https://doi.org/10.1109/ACCESS.2020.2982037.

  Article  Google Scholar 

• Kang, E. J., Hur, C. Y., & Choi, Y. S. (2020). CrowdForest: A visualization tool for opinion sharing based-on semantic figurative metaphors. In proceedings of the 25th international conference on intelligent user interfaces companion (pp. 97-98). ACM. https://doi.org/10.1145/3379336.3381486.

• Karlsen, R., Steen-Johnsen, K., Wollebæk, D., & Enjolras, B. (2017). Echo chamber and trench warfare dynamics in online debates. European Journal of Communication, 32(3), 257–273. https://doi.org/10.1177/0267323117695734.

• Kim, H.-N., Saddik, E., & Abdulmotaleb. (2013). Exploring social tagging for personalized community recommendations. User Modeling & User-Adapted Interaction, 23(2–3), 249–285. https://doi.org/10.1007/s11257-012-9130-3.

  Article  Google Scholar 

• Knijnenburg, B. P., Sivakumar, S., & Wilkinson, D. (2016). Recommender systems for self-actualization. Proceedings of the 10th ACM conference on recommender Systems (pp. 11-14). ACM. https://doi.org/10.1145/2959100.2959189.

• Köhler, S., Wöhner, T., & Peters, R. (2016). The impact of consumer preferences on the accuracy of collaborative filtering recommender systems. Electronic Markets, 26(4), 369–379. https://doi.org/10.1007/s12525-016-0232-3.

  Article  Google Scholar 

• Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 426-434). ACM. https://doi.org/10.1145/1401890.1401944.

• Lee, D., & Hosanagar, K. (2014). Impact of recommender systems on sales volume and diversity. In 35th international conference on information Systems: Building a better world through information Systems (pp. 1-15). AIS.

• Li, B., Yang, Q., & Xue, X. (2009a). Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In twenty-first international joint conference on artificial intelligence (pp. 2052-2057). AIS. https://doi.org/10.5555/1661445.1661773.

• Li, B., Yang, Q., & Xue, X. (2009b). Transfer learning for collaborative filtering via a rating-matrix generative model. In proceedings of the 26th annual international conference on machine learning (pp. 617-624). ACM. https://doi.org/10.1145/1553374.1553454.

• Lu, Z., Wang, H., Mamoulis, N., Tu, W., & Cheung, D. W. (2017). Personalized location recommendation by aggregating multiple recommenders in diversity. GeoInformatica, 21(3), 459–484. https://doi.org/10.1007/s10707-017-0298-x.

  Article  Google Scholar 

• Lunardi, G. M., Machado, G. M., Maran, V., & de Oliveira, J. P. M. (2020). A metric for filter bubble measurement in recommender algorithms considering the news domain. Applied Soft Computing, 97(1), 106771. https://doi.org/10.1016/j.asoc.2020.106771.

  Article  Google Scholar 

• Man, T., Shen, H., Jin, X., & Cheng, X. (2017). Cross-domain recommendation: An embedding and mapping approach. In proceedings of the twenty-sixth international conference on artificial intelligence (pp. 2464-2470). ACM. https://doi.org/10.24963/ijcai.2017/343.

• Medo, M., Zhang, Y.-C., & Zhou, T. (2009). Adaptive model for recommendation of news. Europhysics Letters, 88(3), 38005. https://doi.org/10.1209/0295-5075/88/38005.

  Article  Google Scholar 

• Mirbakhsh, N., & Ling, C. X. (2015). Improving top-n recommendation for cold-start users via cross-domain information. ACM Transactions on Knowledge Discovery from Data, 9(4), 1–19. https://doi.org/10.1145/2724720.

  Article  Google Scholar 

• Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. Advances in Neural Information Processing Systems, 20, 1257–1264. https://doi.org/10.5555/2981562.2981720.

  Article  Google Scholar 

• Mueller, D. C. (2003). Public choice III. Cambridge University Press. https://doi.org/10.1017/CBO9780511813771.

• Nagulendra, S., & Vassileva, J. (2014). Understanding and controlling the filter bubble through interactive visualization: A user study. In proceedings of the 25th ACM conference on hypertext and social media (pp. 107-115). ACM. https://doi.org/10.1145/2631775.2631811.

• Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., & Konstan, J. A. (2014). Exploring the filter bubble: The effect of using recommender systems on content diversity. In proceedings of the 23rd international conference on world wide web (pp. 677-686). ACM. https://doi.org/10.1145/2566486.2568012.

• Nikolov, D., Lalmas, M., Flammini, A., & Menczer, F. (2019). Quantifying biases in online information exposure. Journal of the Association for Information Science Technology, 70(3), 218–229. https://doi.org/10.1002/asi.24121.

• Nikolov, D., Oliveira, D. F., Flammini, A., & Menczer, F. (2015). Measuring online social bubbles. Peer J Computer Science, 1, e38. https://doi.org/10.7717/peerj-cs.38.

  Article  Google Scholar 

• Pan, W., Xiang, E. W., Liu, N. N., & Yang, Q. (2010). Transfer learning in collaborative filtering for sparsity reduction. In proceedings of the AAAI conference on artificial intelligence (pp. 230-235). AAAI. https://doi.org/10.5555/2898607.2898644.

• Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin UK. https://doi.org/10.3139/9783446431164.

• Pilászy, I., Zibriczky, D., & Tikk, D. (2010). Fast als-based matrix factorization for explicit and implicit feedback datasets. Proceedings of the fourth ACM conference on recommender systems (pp. 71-78). ACM. https://doi.org/10.1145/1864708.1864726.

• Qin, L., & Zhu, X. (2013). Promoting diversity in recommendation by entropy regularizer. In proceedings of the twenty-third international joint conference on artificial intelligence (pp. 2698-2704). ACM.

• Rastegarpanah, B., Gummadi, K. P., & Crovella, M. (2019). Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. Proceedings of the twelfth ACM international conference on web search and data mining (pp. 231-239). ACM. https://doi.org/10.1145/3289600.3291002.

• Resnick, P., Garrett, R. K., Kriplean, T., Munson, S. A., & Stroud, N. J. (2013). Bursting your (filter) bubble: Strategies for promoting diverse exposure. In proceedings of the 2013 conference on computer supported cooperative work companion (pp. 95-100). ACM. https://doi.org/10.1145/2441955.2441981.

• Ridgway, R. (2017). Against a personalisation of the self. Ephemera: Theory & Politics in Organization, 17(2), 377–379.

  Google Scholar 

• Rowland, F. (2011). The filter bubble: What the internet is hiding from you. Portal: Libraries the Academy, 11(4), 1009–1011. https://doi.org/10.1353/pla.2011.0036.

  Article  Google Scholar 

• Schomakers, E.-M., Lidynia, C., & Ziefle, M. (2020). All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity. Electronic Markets, 30(3), 649–665. https://doi.org/10.1007/s12525-020-00404-9.

  Article  Google Scholar 

• Severin, W. J., & Tankard, J. W. (1997). Communication theories: Origins, methods, and uses in the mass media. Longman.

  Google Scholar 

• Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

  Article  Google Scholar 

• Shevade, S., & Murty, M. (2019). Neural cross-domain collaborative filtering with shared entities. Information Retrieval, 1, 729–745. https://doi.org/10.1007/978-3-030-67658-2_42.

  Article  Google Scholar 

• Singh, A. P., & Gordon, G. J. (2008). Relational learning via collective matrix factorization. In proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 650-658). ACM. https://doi.org/10.1145/1401890.1401969.

• Su, J., Sharma, A., & Goel, S. (2016). The effect of recommendations on network structure. In proceedings of the 25th international conference on world wide web (pp. 1157-1167). ACM. https://doi.org/10.1145/2872427.2883040.

• Sun, J., Song, J., Jiang, Y., Liu, Y., & Zhu, M. (2020). Leveraging cross domain recommendation models to alleviate filter bubble problems. Proceedings of the 26th Americas conference on information Systems (pp. 1-10). AMCIS.

• Symeonidis, P., Coba, L., & Zanker, M. (2019). Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization. Intelligenza Artificiale, 13(1), 37–47. https://doi.org/10.3233/IA-190017.

  Article  Google Scholar 

• Tang, J., Gao, H., & Liu, H. (2012). mTrust: Discerning multi-faceted trust in a connected world. In proceedings of the fifth ACM international conference on web search and data mining (pp. 93-102). ACM. https://doi.org/10.1145/2124295.2124309.

• Taramigkou, M., Bothos, E., Christidis, K., Apostolou, D., & Mentzas, G. (2013). Escape the bubble: Guided exploration of music preferences for serendipity and novelty. In proceedings of the 7th ACM conference on recommender systems (pp. 335-338). ACM. https://doi.org/10.1145/2507157.2507223.

• Thonet, T., Cabanac, G., Boughanem, M., & Pinel-Sauvagnat, K. (2017). Users are known by the company they keep: Topic models for viewpoint discovery in social networks. In proceedings of the 2017 ACM on conference on information and knowledge management (pp. 87-96). ACM. https://doi.org/10.1145/3132847.3132897.

• Tk, A. K., George, K., & Thomas, J. P. (2015). An empirical approach to detection of topic bubbles in tweets. In 2015 IEEE/ACM 2nd international symposium on big data computing (BDC) (pp. 31-40). IEEE. https://doi.org/10.1109/BDC.2015.36.

• Vargas, S., Baltrunas, L., Karatzoglou, A., & Castells, P. (2014). Coverage, redundancy and size-awareness in genre diversity for recommender systems. In proceedings of the 8th ACM conference on recommender systems (pp. 209-216). ACM. https://doi.org/10.1145/2645710.2645743.

• Vargas, S., & Castells, P. (2013). Exploiting the diversity of user preferences for recommendation. In proceedings of the 10th conference on open research areas in information retrieval (pp. 129-136). ACM. https://doi.org/10.5555/2491748.2491776.

• Wang, X., Peng, Z., Wang, S., Philip, S. Y., Fu, W., Xu, X., Hong, X. J. K., & Systems, I. (2019). CDLFM: Cross-domain recommendation for cold-start users via latent feature mapping. Knowledge and Information Systems, 62, 1723–1750. https://doi.org/10.1007/s10115-019-01396-5.

  Article  Google Scholar 

• Wardle, C., & Williams, A. (2010). Beyond user-generated content: A production study examining the ways in which UGC is used at the BBC. Media, Culture Society, 32(5), 781–799. https://doi.org/10.1177/0163443710373953.

• Wartena, C., Brussee, R., & Wibbels, M. (2009). Using tag co-occurrence for recommendation. In 2009 ninth international conference on intelligent Systems design and applications (pp. 273-278). IEEE. https://doi.org/10.1109/ISDA.2009.130.

• Wasilewski, J., & Hurley, N. (2016). Incorporating diversity in a learning to rank recommender system. In Preceedings of the twenty-ninth international Florida artificial intelligence research society conference (pp. 572-578). AAAI.

• Wasilewski, J., & Hurley, N. (2018). Intent-aware item-based collaborative filtering for personalised diversification. In proceedings of the 26th conference on user modeling, adaptation and personalization (pp. 81-89). ACM. https://doi.org/10.1145/3209219.3209234.

• Wu, Q., Liu, Y., Miao, C., Zhao, B., Zhao, Y., & Guan, L. (2019). PD-GAN: Adversarial learning for personalized diversity-promoting recommendation. In proceedings of the Twenth-eighth international joint conference on artificial intelligence (pp. 3870-3876). IJCAI. https://doi.org/10.24963/ijcai.2019/ 537.

• Wu, W., Chen, L., & Zhao, Y. (2018). Personalizing recommendation diversity based on user personality. User Modeling & User-Adapted Interaction, 28(3), 237–276. https://doi.org/10.1007/s11257-018-9205-x.

  Article  Google Scholar 

• Xia, H., Wei, X., An, W., Zhang, Z. J., & Sun, Z. (2020). Design of electronic-commerce recommendation systems based on outlier mining. Electronic markets, 1-17. https://doi.org/10.1007/s12525-020-00435-2.

• Xue, H.-J., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017). Deep matrix factorization models for recommender Systems. In proceedings of the Twenth-sixth international joint conference on artificial intelligence (pp. 3203-3209). IJCAI. https://doi.org/10.24963/ijcai.2017/447.

• Zhang, C., Yu L., Wang, Y., Shah, C., & Zhang, X. (2017). Collaborative user network embedding for social recommender Systems. In proceedings of the 17th SIAM international conference on data mining (pp. 381-389). SIAM. https://doi.org/10.1137/1.9781611974973.43.

• Zhang, H., Wei, S., Hu, X., Li, Y., & Xu, J. (2020). On accurate POI recommendation via transfer learning. Distributed Parallel Databases, 38(3), 585–599. https://doi.org/10.1007/s10619-020-07299-7.

  Article  Google Scholar 

• Zhang, M., & Hurley, N. (2008). Avoiding monotony: Improving the diversity of recommendation lists. In proceedings of the 2008 ACM conference on recommender systems (pp. 123-130). ACM. https://doi.org/10.1145/1454008.1454030.

• Zhang, Y., Abbas, H., & Sun, Y. (2019). Smart e-commerce integration with recommender systems. Electronic Markets, 29(2), 219–220. https://doi.org/10.1007/s12525-019-00346-x.

  Article  Google Scholar 

• Zhao, C., Li, C., Xiao, R., Deng, H., & Sun, A. (2020). CATN: Cross-domain recommendation for cold-start users via aspect transfer network. In proceedings of the 43rd international ACM SIGIR conference on Research and Development in information retrieval (pp. 229-338). ACM. https://doi.org/10.1145/3397271.3401169.

• Zhao, J., Lui, J. C., Towsley, D., Guan, X., & Zhou, Y. (2011). Empirical analysis of the evolution of follower network: A case study on Douban. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 924-929). IEEE. https://doi.org/10.1109/INFCOMW.2011.5928945.

• Zhong, E., Fan, W., & Yang, Q. (2014). User behavior learning and transfer in composite social networks. ACM Transactions on Knowledge Discovery from Data, 8(1), 1–32. https://doi.org/10.1145/2556613.

  Article  Google Scholar 

• Zhou, J. T., Pan, S. J., & Tsang, I. W. (2019). A deep learning framework for hybrid heterogeneous transfer learning. Artificial Intelligence, 275, 310–328. https://doi.org/10.1016/j.artint.2019.06.001.

  Article  Google Scholar 

• Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10), 4511–4515. https://doi.org/10.1073/pnas.1000488107.

  Article  Google Scholar 

• Zimmer, F., Scheibe, K., Stock, M., & Stock, W. (2019). Echo chambers and filter bubbles of fake news in social media: Man-made or produced by algorithms? In 2019 Hawaii University international conferences in arts, humanities, social sciences & education (pp. 1-22). HUIC. https://doi.org/10.3886/E135024V2.

Download references