Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations (2307.02797v1)

Published 6 Jul 2023 in cs.IR and cs.AI

Abstract: In the realm of personalized recommendation systems, the increasing concern is the amplification of belief imbalance and user biases, a phenomenon primarily attributed to the filter bubble. Addressing this critical issue, we introduce an innovative intermediate agency (BHEISR) between users and existing recommendation systems to attenuate the negative repercussions of the filter bubble effect in extant recommendation systems. The main objective is to strike a belief balance for users while minimizing the detrimental influence caused by filter bubbles. The BHEISR model amalgamates principles from nudge theory while upholding democratic and transparent principles. It harnesses user-specific category information to stimulate curiosity, even in areas users might initially deem uninteresting. By progressively stimulating interest in novel categories, the model encourages users to broaden their belief horizons and explore the information they typically overlook. Our model is time-sensitive and operates on a user feedback loop. It utilizes the existing recommendation algorithm of the model and incorporates user feedback from the prior time frame. This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements. To validate the effectiveness and reliability of the BHEISR model, we conducted a series of comprehensive experiments with real-world datasets. These experiments compared the performance of the BHEISR model against several baseline models using nearly 200 filter bubble-impacted users as test subjects. Our experimental results conclusively illustrate the superior performance of the BHEISR model in mitigating filter bubbles and balancing user perspectives.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simulation Modelling Practice and Theory 113 (2021), 102375.
  2. A survey on echo chambers on social media: Description, detection and mitigation. arXiv preprint arXiv:2112.05084 (2021).
  3. Personalized bundle list recommendation. In The World Wide Web Conference. 60–71.
  4. Michael Barbehenn. 1998. A note on the complexity of Dijkstra’s algorithm for graphs with weighted vertices. IEEE transactions on computers 47, 2 (1998), 263.
  5. David Bawden and Lyn Robinson. 2020. Information overload: An overview. (2020).
  6. John Beshears and Harry Kosowsky. 2020. Nudging: Progress to date and future directions. Organizational behavior and human decision processes 161 (2020), 3–19.
  7. Jennifer Blumenthal-Barby and Douglas J Opel. 2018. Nudge or grudge? Choice architecture and parental decision-making. Hastings Center Report 48, 2 (2018), 33–39.
  8. Lauren Valentino Bryant. 2020. The YouTube algorithm and the alt-right filter bubble. Open Information Science 4, 1 (2020), 85–90.
  9. Ideological belief bias with political syllogisms. Thinking & Reasoning 26, 2 (2020), 291–310.
  10. Novelty and diversity in recommender systems. In Recommender systems handbook. Springer, 603–646.
  11. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2023), 1–39.
  12. Uthsav Chitra and Christopher Musco. 2020. Analyzing the impact of filter bubbles on social network polarization. In Proceedings of the 13th International Conference on Web Search and Data Mining. 115–123.
  13. Peter M Dahlgren. 2021. A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review 42, 1 (2021), 15–33.
  14. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
  15. Axel Ekström. 2021. The self-imposed filter bubble hypothesis. (2021).
  16. Community-based recommendations on Twitter: avoiding the filter bubble. In Web Information Systems Engineering–WISE 2019: 20th International Conference, Hong Kong, China, January 19–22, 2020, Proceedings 20. Springer, 212–227.
  17. Reducing the filter bubble effect on Twitter by considering communities for recommendations. International Journal of Web Information Systems 17, 6 (2021), 728–752.
  18. An attentional recurrent neural network for personalized next location recommendation. In Proceedings of the AAAI Conference on artificial intelligence, Vol. 34. 83–90.
  19. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3549–3568.
  20. AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society. ([n. d.]).
  21. Mathias Jesse and Dietmar Jannach. 2021. Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports 3 (2021), 100052.
  22. A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes. Sensors 22, 12 (2022), 4620.
  23. Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption. MIS quarterly 44, 4 (2020).
  24. Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System. In Proceedings of the ACM Web Conference 2023. 4041–4049.
  25. A metric for Filter Bubble measurement in recommender algorithms considering the news domain. Applied Soft Computing 97 (2020), 106771.
  26. What Are Filter Bubbles Really? A Review of the Conceptual and Empirical Work. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 274–279.
  27. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019).
  28. Zachary A Pardos and Weijie Jiang. 2019. Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System. (2019).
  29. Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the web conference 2020. 1194–1204.
  30. Ganesan Ramalingam and Thomas Reps. 1993. A categorized bibliography on incremental computation. In Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages. 502–510.
  31. Content-based movie recommendation system using genre correlation. In Smart Intelligent Computing and Applications: Proceedings of the Second International Conference on SCI 2018, Volume 2. Springer, 391–397.
  32. Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion. 95–100.
  33. A knowledge-driven digital nudging approach to recommender systems built on a modified Onicescu method. Expert Systems with Applications 181 (2021), 115170.
  34. Diversifying dialogue generation with non-conversational text. arXiv preprint arXiv:2005.04346 (2020).
  35. CareGraph: A Graph-based Recommender System for Diabetes Self-Care. ([n. d.]).
  36. A new belief entropy based on Deng entropy. Entropy 21, 10 (2019), 987.
  37. Robust collaborative filtering recommendation with user-item-trust records. IEEE Transactions on Computational Social Systems 9, 4 (2021), 986–996.
  38. User-controllable Recommendation Against Filter Bubbles. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1251–1261.
  39. On scalability of association-rule-based recommendation: A unified distributed-computing framework. ACM Transactions on the Web (TWEB) 14, 3 (2020), 1–21.
  40. Fuyuan Xiao. 2019. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy. Information Fusion 46 (2019), 23–32.
  41. DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 661–669.
  42. It takes variety to make a world: diversification in recommender systems. In Proceedings of the 12th international conference on extending database technology: Advances in database technology. 368–378.
  43. A generalized belief entropy with nonspecificity and structural conflict. IEEE Transactions on Systems, Man, and Cybernetics: Systems 52, 9 (2021), 5532–5545.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Mengyan Wang (3 papers)
  2. Yuxuan Hu (35 papers)
  3. Zihan Yuan (3 papers)
  4. Chenting Jiang (1 paper)
  5. Weihua Li (43 papers)
  6. Shiqing Wu (7 papers)
  7. Quan Bai (23 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now