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Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review (2307.01221v1)

Published 2 Jul 2023 in cs.IR and cs.AI

Abstract: A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.

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References (105)
  1. A. Sayed, Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Intelligent edge-based recommender system for internet of energy applications,” IEEE Systems Journal, 2021.
  2. S. Atalla, M. Daradkeh, A. Gawanmeh, H. Khalil, W. Mansoor, S. Miniaoui, and Y. Himeur, “An intelligent recommendation system for automating academic advising based on curriculum analysis and performance modeling,” Mathematics, vol. 11, no. 5, p. 1098, 2023.
  3. Y. Himeur, A. Alsalemi, A. Al-Kababji, F. Bensaali, A. Amira, C. Sardianos, G. Dimitrakopoulos, and I. Varlamis, “A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects,” Information Fusion, vol. 72, pp. 1–21, 2021.
  4. I. Varlamis, C. Sardianos, C. Chronis, G. Dimitrakopoulos, Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Smart fusion of sensor data and human feedback for personalized energy-saving recommendations,” Applied Energy, vol. 305, p. 117775, 2022.
  5. P. Dokoupil, “Long-term fairness for group recommender systems with large groups,” in Proceedings of the 16th ACM Conference on Recommender Systems, 2022, pp. 724–726.
  6. F. Tahmasebi, M. Meghdadi, S. Ahmadian, and K. Valiallahi, “A hybrid recommendation system based on profile expansion technique to alleviate cold start problem,” Multimedia Tools and Applications, vol. 80, no. 2, pp. 2339–2354, 2021.
  7. Z. Ali, G. Qi, K. Muhammad, S. Bhattacharyya, I. Ullah, and W. Abro, “Citation recommendation employing heterogeneous bibliographic network embedding,” Neural Computing and Applications, vol. 34, no. 13, pp. 10 229–10 242, 2022.
  8. H.-h. Wu, G. Ke, Y. Wang, and Y.-T. Chang, “Prediction on recommender system based on bi-clustering and moth flame optimization,” Applied Soft Computing, vol. 120, p. 108626, 2022.
  9. C. Sardianos, I. Varlamis, G. Dimitrakopoulos, D. Anagnostopoulos, A. Alsalemi, F. Bensaali, Y. Himeur, and A. Amira, “Rehab-c: Recommendations for energy habits change,” Future Generation Computer Systems, vol. 112, pp. 394–407, 2020.
  10. C. Wu, F. Wu, T. Qi, C. Li, and Y. Huang, “Is news recommendation a sequential recommendation task?” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 2382–2386.
  11. M. Arif, S. S. Sohail, M. T. Alam, S. Ubaid, M. T. Nafis, G. Wang et al., “Towards a two-tier architecture for privacy-enabled recommender systems (pers),” in Inernational Conference on Ubiquitous Security.   Springer, 2021, pp. 268–278.
  12. Y. Himeur, A. Sayed, A. Alsalemi, F. Bensaali, A. Amira, I. Varlamis, M. Eirinaki, C. Sardianos, and G. Dimitrakopoulos, “Blockchain-based recommender systems: Applications, challenges and future opportunities,” Computer Science Review, vol. 43, p. 100439, 2022.
  13. L. Boratto, G. Fenu, and M. Marras, “The effect of algorithmic bias on recommender systems for massive open online courses,” in European Conference on Information Retrieval.   Springer, 2019, pp. 457–472.
  14. J. Protasiewicz, W. Pedrycz, M. Kozłowski, S. Dadas, T. Stanisławek, A. Kopacz, and M. Gałężewska, “A recommender system of reviewers and experts in reviewing problems,” Knowledge-Based Systems, vol. 106, pp. 164–178, 2016.
  15. Y. Himeur, S. S. Sohail, F. Bensaali, A. Amira, and M. Alazab, “Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives,” Computers & Security, p. 102746, 2022.
  16. M. Ashraf, S. Fatima, M. Fatma, S. Muntaha, U. Rooman, S. S. Sohail, and A. Sarosh, “Private browsing does not affect google personalization: An experimental evaluation,” in Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications: ICAIAA 2022.   Springer, 2023, pp. 457–465.
  17. J. Chen, H. Dong, X. Wang, F. Feng, M. Wang, and X. He, “Bias and debias in recommender system: A survey and future directions,” arXiv preprint arXiv:2010.03240, 2020.
  18. Z. Gao, T. Shen, Z. Mai, M. R. Bouadjenek, I. Waller, A. Anderson, R. Bodkin, and S. Sanner, “Mitigating the filter bubble while maintaining relevance: Targeted diversification with vae-based recommender systems,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 2524–2531.
  19. E. Olshannikova, E. Skenderi, T. Olsson, S. Koivunen, and J. Huhtamäki, “Utilizing structural network positions to diversify people recommendations on twitter,” Advances in Human-Computer Interaction, vol. 2022, 2022.
  20. M. Alam, A. Iana, A. Grote, K. Ludwig, P. Müller, and H. Paulheim, “Towards analyzing the bias of news recommender systems using sentiment and stance detection,” in Companion Proceedings of the Web Conference 2022, 2022, pp. 448–457.
  21. W. Cai, F. Feng, Q. Wang, T. Yang, Z. Liu, and C. Xu, “A causal view for item-level effect of recommendation on user preference,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023, pp. 240–248.
  22. M. Hildebrandt, “The issue of proxies and choice architectures. why eu law matters for recommender systems,” Frontiers in Artificial Intelligence, p. 73, 2022.
  23. D. Spohr, “Fake news and ideological polarization: Filter bubbles and selective exposure on social media,” Business information review, vol. 34, no. 3, pp. 150–160, 2017.
  24. M. Ćurković and A. Košec, “(re) search filter bubble effect—an issue still unfairly neglected,” Advances in Nutrition, vol. 11, no. 3, pp. 744–744, 2020.
  25. J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-based systems, vol. 46, pp. 109–132, 2013.
  26. F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender systems handbook.   Springer, 2011, pp. 1–35.
  27. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems (TOIS), vol. 22, no. 1, pp. 5–53, 2004.
  28. B. Smyth and P. McClave, “Similarity vs. diversity,” in International conference on case-based reasoning.   Springer, 2001, pp. 347–361.
  29. S. M. McNee, J. Riedl, and J. A. Konstan, “Being accurate is not enough: how accuracy metrics have hurt recommender systems,” in CHI’06 extended abstracts on Human factors in computing systems, 2006, pp. 1097–1101.
  30. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th international conference on World Wide Web, 2005, pp. 22–32.
  31. P. Adamopoulos and A. Tuzhilin, “On unexpectedness in recommender systems: Or how to better expect the unexpected,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 4, pp. 1–32, 2014.
  32. G. Adomavicius and Y. Kwon, “Improving aggregate recommendation diversity using ranking-based techniques,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 5, pp. 896–911, 2011.
  33. Ò. Celma and P. Herrera, “A new approach to evaluating novel recommendations,” in Proceedings of the 2008 ACM conference on Recommender systems, 2008, pp. 179–186.
  34. N. Hurley and M. Zhang, “Novelty and diversity in top-n recommendation–analysis and evaluation,” ACM Transactions on Internet Technology (TOIT), vol. 10, no. 4, pp. 1–30, 2011.
  35. S. Vargas and P. Castells, “Rank and relevance in novelty and diversity metrics for recommender systems,” in Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 109–116.
  36. A. Bruns, “Filter bubble,” Internet Policy Review, vol. 8, 2019.
  37. P. Resnick, R. K. Garrett, T. Kriplean, S. A. Munson, and N. J. Stroud, “Bursting your (filter) bubble: strategies for promoting diverse exposure,” in Proceedings of the 2013 conference on Computer supported cooperative work companion, 2013, pp. 95–100.
  38. A. Amrollahi et al., “A conceptual tool to eliminate filter bubbles in social networks,” Australasian Journal of Information Systems, vol. 25, 2021.
  39. L. Terren and R. Borge-Bravo, “Echo chambers on social media: a systematic review of the literature,” Review of Communication Research, vol. 9, pp. 99–118, 2021.
  40. A. Miller, S. Arndt, L. Engel, and N. Boot, “Nature conservation in a digitalized world: echo chambers and filter bubbles,” Ecology and Society, vol. 26, no. 3, 2021.
  41. K. M. Kuehn and L. A. Salter, “Assessing digital threats to democracy, and workable solutions: a review of the recent literature,” International Journal of Communication, vol. 14, p. 22, 2020.
  42. H. Roosenschoon and E. Loos, “Maatschappelijke impact van gepersonaliseerde informatievoorziening: feit of fictie?” Mens & Maatschappij, vol. 95, no. 2, pp. 133–150, 2020.
  43. B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” 2007.
  44. R. Karlsen, K. Steen-Johnsen, D. Wollebæk, and B. Enjolras, “Echo chamber and trench warfare dynamics in online debates,” European journal of communication, vol. 32, no. 3, pp. 257–273, 2017.
  45. F. Rowland, “The filter bubble: what the internet is hiding from you,” portal: Libraries and the Academy, vol. 11, no. 4, pp. 1009–1011, 2011.
  46. A. Gharahighehi and C. Vens, “Diversification in session-based news recommender systems,” Personal and Ubiquitous Computing, pp. 1–11, 2021.
  47. D. Fleder and K. Hosanagar, “Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity,” Management science, vol. 55, no. 5, pp. 697–712, 2009.
  48. D. Lee and K. Hosanagar, “Impact of recommender systems on sales volume and diversity,” 2014.
  49. G. Eady, J. Nagler, A. Guess, J. Zilinsky, and J. A. Tucker, “How many people live in political bubbles on social media? evidence from linked survey and twitter data,” Sage Open, vol. 9, no. 1, p. 2158244019832705, 2019.
  50. E.-M. Schomakers, C. Lidynia, and M. Ziefle, “All of me? users’ preferences for privacy-preserving data markets and the importance of anonymity,” Electronic Markets, vol. 30, no. 3, pp. 649–665, 2020.
  51. F. Zimmer, K. Scheibe, M. Stock, and W. G. Stock, “Echo chambers and filter bubbles of fake news in social media. man-made or produced by algorithms,” in 8th annual arts, humanities, social sciences & education conference, 2019, pp. 1–22.
  52. P. Symeonidis, L. Coba, and M. Zanker, “Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization,” Intelligenza Artificiale, vol. 13, no. 1, pp. 37–47, 2019.
  53. S. Monteiro Machado, “Democracia em risco? explorando a contribuição do ciberjornalismo para o fenómeno do filtro-bolha.” Observatorio (OBS*), vol. 15, no. 2, 2021.
  54. N. Wang and L. Chen, “User bias in beyond-accuracy measurement of recommendation algorithms,” in Fifteenth ACM Conference on Recommender Systems, 2021, pp. 133–142.
  55. Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, and Y. Zhang, “Understanding echo chambers in e-commerce recommender systems,” in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 2020, pp. 2261–2270.
  56. F. Lorenz, J. Yuan, A. Lommatzsch, M. Mu, N. Race, F. Hopfgartner, and S. Albayrak, “Countering contextual bias in tv watching behavior: introducing social trend as external contextual factor in tv recommenders,” in Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video, 2017, pp. 21–30.
  57. A. Antikacioglu and R. Ravi, “Post processing recommender systems for diversity,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 707–716.
  58. K. Hosanagar, D. Fleder, D. Lee, and A. Buja, “Will the global village fracture into tribes? recommender systems and their effects on consumer fragmentation,” Management Science, vol. 60, no. 4, pp. 805–823, 2014.
  59. C. Matt, A. Benlian, T. Hess, and C. Weiß, “Escaping from the filter bubble? the effects of novelty and serendipity on users’ evaluations of online recommendations,” 2014.
  60. S. Vrijenhoek, M. Kaya, N. Metoui, J. Möller, D. Odijk, and N. Helberger, “Recommenders with a mission: assessing diversity in news recommendations,” in Proceedings of the 2021 Conference on Human Information Interaction and Retrieval, 2021, pp. 173–183.
  61. T. Donkers and J. Ziegler, “The dual echo chamber: Modeling social media polarization for interventional recommending,” in Fifteenth ACM Conference on Recommender Systems, 2021, pp. 12–22.
  62. A. Tommasel, J. M. Rodriguez, and D. Godoy, “I want to break free! recommending friends from outside the echo chamber,” in Fifteenth ACM Conference on Recommender Systems, 2021, pp. 23–33.
  63. W. Wu, L. Chen, and Y. Zhao, “Personalizing recommendation diversity based on user personality,” User Modeling and User-Adapted Interaction, vol. 28, no. 3, pp. 237–276, 2018.
  64. M. Makhortykh and M. Wijermars, “Can filter bubbles protect information freedom? discussions of algorithmic news recommenders in eastern europe,” Digital Journalism, pp. 1–25, 2021.
  65. G. M. Lunardi, G. M. Machado, V. Maran, and J. P. M. de Oliveira, “A metric for filter bubble measurement in recommender algorithms considering the news domain,” Applied Soft Computing, vol. 97, p. 106771, 2020.
  66. G. Aridor, D. Goncalves, and S. Sikdar, “Deconstructing the filter bubble: User decision-making and recommender systems,” in Fourteenth ACM Conference on Recommender Systems, 2020, pp. 82–91.
  67. N. Polatidis, A. Papaleonidas, E. Pimenidis, and L. Iliadis, “An explanation-based approach for experiment reproducibility in recommender systems,” Neural Computing and Applications, vol. 32, no. 16, pp. 12 259–12 266, 2020.
  68. S. Milano, M. Taddeo, and L. Floridi, “Recommender systems and their ethical challenges,” Ai & Society, vol. 35, no. 4, pp. 957–967, 2020.
  69. A. Gharahighehi and C. Vens, “Personalizing diversity versus accuracy in session-based recommender systems,” SN Computer Science, vol. 2, no. 1, pp. 1–12, 2021.
  70. P.-R. Lhérisson, F. Muhlenbach, and P. Maret, “Fair recommendations through diversity promotion,” in International Conference on Advanced Data Mining and Applications.   Springer, 2017, pp. 89–103.
  71. A. Gharahighehi and C. Vens, “Making session-based news recommenders diversity-aware,” in Proceedings of the Workshop on Online Misinformation-and Harm-Aware Recommender Systems.   CEUR Workshop Proceedings, 2020, pp. 60–66.
  72. G. Joris, C. Colruyt, J. Vermeulen, S. Vercoutere, F. D. Grove, K. V. Damme, O. D. Clercq, C. V. Hee, L. D. Marez, V. Hoste et al., “News diversity and recommendation systems: setting the interdisciplinary scene,” in IFIP International Summer School on Privacy and Identity Management.   Springer, 2019, pp. 90–105.
  73. T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, “Enhancement of the neutrality in recommendation.” in Decisions@ RecSys.   Citeseer, 2012, pp. 8–14.
  74. ——, “Efficiency improvement of neutrality-enhanced recommendation.” in Decisions@ RecSys.   Citeseer, 2013, pp. 1–8.
  75. T. T. Nguyen, P.-M. Hui, F. M. Harper, L. Terveen, and J. A. Konstan, “Exploring the filter bubble: the effect of using recommender systems on content diversity,” in Proceedings of the 23rd international conference on World wide web, 2014, pp. 677–686.
  76. N. Helberger, K. Karppinen, and L. D’acunto, “Exposure diversity as a design principle for recommender systems,” Information, Communication & Society, vol. 21, no. 2, pp. 191–207, 2018.
  77. L. E. Celis, S. Kapoor, F. Salehi, and N. Vishnoi, “Controlling polarization in personalization: An algorithmic framework,” in Proceedings of the conference on fairness, accountability, and transparency, 2019, pp. 160–169.
  78. F. Abbas, N. Najjar, and D. Wilson, “The bites eclectic: Critique-based conversational recommendation for diversity-focused meal planning,” in International Conference on Case-Based Reasoning.   Springer, 2021, pp. 1–16.
  79. G. M. Lunardi, “Representing the filter bubble: Towards a model to diversification in news,” in International Conference on Conceptual Modeling.   Springer, 2019, pp. 239–246.
  80. J. Sun, J. Song, Y. Jiang, Y. Liu, and J. Li, “Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization,” Electronic Markets, pp. 1–21, 2021.
  81. Y. Zhao, C. Wang, H. Han, M. Shu, and W. Wang, “An impact evaluation framework of personalized news aggregation and recommendation systems,” in 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).   IEEE, 2020, pp. 893–900.
  82. J. Sun, J. Song, Y. Jiang, Y. Liu, and M. Zhu, “Leveraging cross domain recommendation models to alleviate filter bubble problems,” 2020.
  83. R. Baeza-Yates, “Bias on the web,” Communications of the ACM, vol. 61, no. 6, pp. 54–61, 2018.
  84. A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization. in advances in neural information processing systems,” 2007.
  85. A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins et al., “Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,” Information fusion, vol. 58, pp. 82–115, 2020.
  86. G. Peake and J. Wang, “Explanation mining: Post hoc interpretability of latent factor models for recommendation systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 2060–2069.
  87. A. Ghazimatin, O. Balalau, R. Saha Roy, and G. Weikum, “Prince: Provider-side interpretability with counterfactual explanations in recommender systems,” in Proceedings of the 13th International Conference on Web Search and Data Mining, 2020, pp. 196–204.
  88. R. Yera, A. A. Alzahrani, and L. Martínez, “Exploring post-hoc agnostic models for explainable cooking recipe recommendations,” Knowledge-Based Systems, vol. 251, p. 109216, 2022.
  89. M. Kunaver and T. Požrl, “Diversity in recommender systems–a survey,” Knowledge-based systems, vol. 123, pp. 154–162, 2017.
  90. T. Eftimov, B. Paudel, G. Popovski, and D. Kocev, “A framework for evaluating personalized ranking systems by fusing different evaluation measures,” Big Data Research, vol. 25, p. 100211, 2021.
  91. L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun, “Temporal recommendation on graphs via long-and short-term preference fusion,” in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 723–732.
  92. J. B. Schafer, J. A. Konstan, and J. Riedl, “Meta-recommendation systems: user-controlled integration of diverse recommendations,” in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 43–51.
  93. M. Marras, L. Boratto, G. Ramos, and G. Fenu, “Equality of learning opportunity via individual fairness in personalized recommendations,” International Journal of Artificial Intelligence in Education, vol. 32, no. 3, pp. 636–684, 2022.
  94. C. Sharma, P. Bedi et al., “Explanation-based serendipitous recommender system (ebsrs),” in International Conference on Innovative Computing and Communications.   Springer, 2022, pp. 1–18.
  95. P. Castells, N. Hurley, and S. Vargas, “Novelty and diversity in recommender systems,” in Recommender systems handbook.   Springer, 2022, pp. 603–646.
  96. H.-W. Liu, J.-Z. Wu, and F.-L. Wu, “An app-based recommender system based on contrasting automobiles,” Processes, vol. 11, no. 3, p. 881, 2023.
  97. A. Hussain and A. Sheikh, “Opportunities for artificial intelligence–enabled social media analysis of public attitudes toward covid-19 vaccines,” NEJM Catalyst Innovations in Care Delivery, vol. 2, no. 1, 2021.
  98. C. Sardianos, I. Varlamis, C. Chronis, G. Dimitrakopoulos, A. Alsalemi, Y. Himeur, F. Bensaali, and A. Amira, “The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency,” International Journal of Intelligent Systems, vol. 36, no. 2, pp. 656–680, 2021.
  99. C. Sardianos, C. Chronis, I. Varlamis, G. Dimitrakopoulos, Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Real-time personalised energy saving recommendations,” in 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics).   IEEE, 2020, pp. 366–371.
  100. K. Hirata, D. Amagata, S. Fujita, and T. Hara, “Categorical diversity-aware inner product search,” IEEE Access, vol. 11, pp. 2586–2596, 2023.
  101. N. Wang and L. Chen, “Correction to: How do item features and user characteristics affect users’ perceptions of recommendation serendipity? a cross-domain analysis,” User Modeling and User-Adapted Interaction, vol. 33, no. 3, pp. 767–767, 2023.
  102. S. Atalla, Y. Himeur, W. Mansoor, A. Amira, F. Fadli, A. Copiaco, and S. S. Sohail, “Recommendation system towards residential energy saving based on anomaly detection,” in 2022 5th International Conference on Signal Processing and Information Security (ICSPIS).   IEEE, 2022, pp. 169–174.
  103. L. Shi, S. Li, X. Ding, and Z. Bu, “Selection bias mitigation in recommender system using uninteresting items based on temporal visibility,” Expert Systems with Applications, vol. 213, p. 118932, 2023.
  104. Z. Liu, Y. Fang, and M. Wu, “Mitigating popularity bias for users and items with fairness-centric adaptive recommendation,” ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1–27, 2023.
  105. R. Xu, J. Bhandari, D. Korenkevych, F. Liu, Y. He, A. Nikulkov, and Z. Zhu, “Optimizing long-term value for auction-based recommender systems via on-policy reinforcement learning,” arXiv preprint arXiv:2305.13747, 2023.
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Authors (8)
  1. Qazi Mohammad Areeb (1 paper)
  2. Mohammad Nadeem (8 papers)
  3. Shahab Saquib Sohail (12 papers)
  4. Raza Imam (16 papers)
  5. Faiyaz Doctor (3 papers)
  6. Yassine Himeur (58 papers)
  7. Amir Hussain (75 papers)
  8. Abbes Amira (35 papers)
Citations (19)
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