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PopGR: Popularity reweighting for debiasing in group recommendation

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Abstract

Like common recommender systems, group recommendation usually suffers from popularity bias where popular items are more likely to be suggested and exposed to users over long-tailed ones. The skewed data distribution caused discrimination against a great amount of unpopular items, which will be further intensified during the group decision-making process. Despite previous studies devoted to addressing popularity bias issue in recommendations, rarely have other works concentrated on such problem in group recommender systems. In this paper, we identify the negative impact of item popularity in a causality manner and propose a Popularity Reweighting Framework for Group Recommendation (PopGR). Specifically, a popularity-aware weighting function is adopted to mitigate the bias problem by incorporating the popularity level of items along with their intrinsic characteristics into group modeling. Experiments conducted on two real world benchmark datasets justify the effectiveness of our model to alleviate bias while maintaining reasonable ranking accuracy.

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Availability of Data and Materials

Both datasets analyzed during the current study are available in the following public domain resources: Weeplaces: https://www.yongliu.org/datasets/ Douban: https://sites.google.com/site/erhengzhong/datasets

Notes

  1. https://www.yongliu.org/datasets/

References

  1. Zhu, Z., Wang, J., Caverlee, J.: Measuring and mitigating item under-recommendation bias in personalized ranking systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–458 (2020)

  2. Guo, L., Yin, H., Wang, Q., Cui, B., Huang, Z., Cui, L.: Group recommendation with latent voting mechanism. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 121–132 (2020). IEEE

  3. Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: Semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009)

    Article  Google Scholar 

  4. Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 111–118 (2010)

  5. Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 119–126 (2010)

  6. Cao, D., He, X., Miao, L., An, Y., Yang, C., Hong, R.: Attentive group recommendation. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 645–654 (2018)

  7. Vinh Tran, L., Nguyen Pham, T.-A., Tay, Y., Liu, Y., Cong, G., Li, X.: Interact and decide: Medley of sub-attention networks for effective group recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 255–264 (2019)

  8. Guo, L., Yin, H., Chen, T., Zhang, X., Zheng, K.: Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Trans. Inf. Syst. (TOIS) 40(1), 1–27 (2021)

    Article  Google Scholar 

  9. Sankar, A., Wu, Y., Wu, Y., Zhang, W., Yang, H., Sundaram, H.: Groupim: A mutual information maximization framework for neural group recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1279–1288 (2020)

  10. Abdollahpouri, H., Mansoury, M.: Multi-sided exposure bias in recommendation. Preprint at arXiv:2006.15772 (2020)

  11. Zhu, Z., He, Y., Zhao, X., Zhang, Y., Wang, J., Caverlee, J.: Popularity-opportunity bias in collaborative filtering. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 85–93 (2021)

  12. Xu, C., Zhao, W., Zhao, J., Guan, Z., Song, X., Li, J.: Uncertainty-aware multiview deep learning for internet of things applications. IEEE Trans. Ind. Inform. 19(2), 1456–1466 (2022)

    Article  Google Scholar 

  13. Liang, D., Charlin, L., Blei, D.M.: Causal inference for recommendation. In: Causation: Foundation to Application, Workshop at UAI. AUAI (2016)

  14. Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., Joachims, T.: Recommendations as treatments: Debiasing learning and evaluation. In: International Conference on Machine Learning, pp. 1670–1679 (2016). PMLR

  15. Wang, Y.-X., Agarwal, A., Dudık, M.: Optimal and adaptive off-policy evaluation in contextual bandits. In: International Conference on Machine Learning, pp. 3589–3597 (2017). PMLR

  16. Gruson, A., Chandar, P., Charbuillet, C., McInerney, J., Hansen, S., Tardieu, D., Carterette, B.: Offline evaluation to make decisions about playlistrecommendation algorithms. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 420–428 (2019)

  17. Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking. Preprint at arXiv:1901.07555 (2019)

  18. Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 42–46 (2017)

  19. Bonner, S., Vasile, F.: Causal embeddings for recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104–112 (2018)

  20. Chen, J., Dong, H., Qiu, Y., He, X., Xin, X., Chen, L., Lin, G., Yang, K.: Autodebias: Learning to debias for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 21–30 (2021)

  21. Wang, X., Zhang, R., Sun, Y., Qi, J.: Doubly robust joint learning for recommendation on data missing not at random. In: International Conference on Machine Learning, pp. 6638–6647 (2019). PMLR

  22. Marlin, B.M., Zemel, R.S.: Collaborative prediction and ranking with non-random missing data. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 5–12 (2009)

  23. Marlin, B., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. Preprint at arXiv:1206.5267 (2012)

  24. Chen, J., Wang, C., Ester, M., Shi, Q., Feng, Y., Chen, C.: Social recommendation with missing not at random data. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 29–38 (2018). IEEE

  25. Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X.: Bias and debias in recommender system: A survey and future directions. ACM Trans. Inf. Syst. 41(3), 1–39 (2023)

    Google Scholar 

  26. Krishnan, S., Patel, J., Franklin, M.J., Goldberg, K.: A methodology for learning, analyzing, and mitigating social influence bias in recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 137–144 (2014)

  27. Lederrey, G., West, R.: When sheep shop: measuring herding effects in product ratings with natural experiments. In: Proceedings of the 2018 World Wide Web Conference, pp. 793–802 (2018)

  28. Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., Zhang, Y.: Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11–20 (2021)

  29. Chen, Z., Xiao, R., Li, C., Ye, G., Sun, H., Deng, H.: Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 579–588 (2020)

  30. Krishnan, A., Sharma, A., Sankar, A., Sundaram, H.: An adversarial approach to improve long-tail performance in neural collaborative filtering. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1491–1494 (2018)

  31. Chen, Y.-L., Cheng, L.-C., Chuang, C.-N.: A group recommendation system with consideration of interactions among group members. Expert Syst. Appl. 34(3), 2082–2090 (2008)

    Article  Google Scholar 

  32. Seko, S., Yagi, T., Motegi, M., Muto, S.: Group recommendation using feature space representing behavioral tendency and power balance among members. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 101–108 (2011)

  33. Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Cao, W.: Deep modeling of group preferences for group-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)

  34. Yin, H., Wang, Q., Zheng, K., Li, Z., Yang, J., Zhou, X.: Social influence-based group representation learning for group recommendation. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 566–577 (2019). IEEE

  35. Jia, Y., Gu, Z., Jiang, Z., Gao, C., Yang, J.: Persistent graph stream summarization for real-time graph analytics. WWWJ (2023)

  36. Jia, Y., Lin, M., Wang, Y., Li, J., Chen, K., Siebert, J., Zhang, G.Z., Liao, Q.: Extrapolation over temporal knowledge graph via hyperbolic embedding. CAAI Trans. Intell. Technol. (2023)

  37. Liu, J., Chen, Y., Huang, X., Li, J., Min, G.: Gnn-based long and short term preference modeling for next-location prediction. Inf. Sci. 629, 1–14 (2023)

    Article  Google Scholar 

  38. Qi, Y., Gu, Z., Li, A., Zhang, X., Shafiq, M., Mei, Y., Lin, K.: Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems. Comput. Electr. Eng. 108, 108660 (2023)

    Article  Google Scholar 

  39. Bi, X., Nie, H., Zhang, G., Hu, L., Ma, Y., Zhao, X., Yuan, Y., Wang, G.: Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision. Inf. Process. Manage. 60(2), 103242 (2023)

    Article  Google Scholar 

  40. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46 (2010)

  41. Liu, X., Tian, Y., Ye, M., Lee, W.-C.: Exploring personal impact for group recommendation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 674–683 (2012)

  42. Yuan, Q., Cong, G., Lin, C.-Y.: Com: a generative model for group recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 163–172 (2014)

  43. Song, X., Li, J., Cai, T., Yang, S., Yang, T., Liu, C.: A survey on deep learning based knowledge tracing. Knowl-Based Syst. 258, 110036 (2022)

    Article  Google Scholar 

  44. Pearl, J.: Causality. Cambridge University Press, Los Angeles (2009)

    Book  Google Scholar 

  45. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

  46. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  47. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. Preprint at arXiv:1205.2618 (2012)

  48. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

  49. Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)

    Article  Google Scholar 

  50. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010). JMLR Workshop and Conference Proceedings

  51. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Preprint at arXiv:1412.6980 (2014)

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Funding

This work is supported by the National Natural Science Foundation of China (No. 62102276, 62102277, 62272334), the Natural Science Foundation of Jiangsu Province (BK20210703, BK20210705), the Major Project of Natural Science Research in Universities of Jiangsu Province (20KJA520005) and the Young Scholar Program of Cyrus Tang Foundation.

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Author notes

  1. Junhua Fang, Pingfu Chao, Jianfeng Qu and Ruoqian Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Hailun Zhou, Junhua Fang, Pingfu Chao, Jianfeng Qu & Ruoqian Zhang

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  1. Hailun Zhou
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Contributions

Hailun Zhou wrote the main manuscript text. Junhua Fang, Pingfu Chao, Jianfeng Qu and Ruoqian Zhang participated in model design and technical discussion. All contributing authors reviewed the manuscript.

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Correspondence to Hailun Zhou.

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Zhou, H., Fang, J., Chao, P. et al. PopGR: Popularity reweighting for debiasing in group recommendation. World Wide Web 27, 34 (2024). https://doi.org/10.1007/s11280-024-01272-5

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