Abstract
Integrated ranking system is one of the critical components of industrial recommendation platforms. An integrated ranking system is expected to generate a mix of heterogeneous items from multiple upstream sources. Two main challenges need to be solved in this process, namely, (i) Utility-fairness tradeoff: an integrated ranking system is required to balance the overall platform’s utility and exposure fairness among different sources; (ii) Information utilization from upstream sources: each source sequence has been carefully arranged by its provider, so how to efficiently utilize the source sequential information is important and should be carefully considered by the integrated ranking system. Existing methods generally cannot address these two challenges well. In this paper, we propose an integrated ranking model called Multi-sourced Constrained Ranking (MSCRank). It is a dual RNN-based model managing the utility-fairness tradeoff with multi-task learning, and capturing information in source sequences with a novel MA-GRU cell. We compare MSCRank with various baselines on public and industrial datasets, and MSCRank achieves the state-of-the-art performance on both utility and fairness. Online A/B test further validates the effectiveness of MSCRank.
Y. Liu, W. Liu and W. Xia—Authors contributed equally to this research.
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Notes
- 1.
Code is available in https://github.com/sjtulyf123/MSCRank.
References
Mindspore (2020). https://www.mindspore.cn/
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR (1998)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Clarke, C.L., et al.: Novelty and diversity in information retrieval evaluation. In: Proceedings of SIGIR (2008)
Fu, M., Agrawal, A., Irissappane, A.A., Zhang, J., Huang, L., Qu, H.: Deep reinforcement learning framework for category-based item recommendation. IEEE Trans. Cybern. 52(11), 12028–12041 (2021)
Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of of KDD (2019)
Kullback, S.: Information theory and statistics. Courier Corporation (1997)
Morik, M., Singh, A., Hong, J., Joachims, T.: Controlling fairness and bias in dynamic learning-to-rank. In: Proceedings of SIGIR (2020)
Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: Proceedings of KDD (2017)
Pei, C., et al.: Personalized re-ranking for recommendation (2019)
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of WWW (2007)
Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Proceedings of NeurIPS (2018)
Sonboli, N., et al.: Librec-auto: a tool for recommender systems experimentation. In: Proceedings of CIKM (2021)
Wan, M., Ni, J., Misra, R., McAuley, J.: Addressing marketing bias in product recommendations. In: Proceedings of WSDM (2020)
Xi, Y., et al.: On-device integrated re-ranking with heterogeneous behavior modeling. In: Proceedings of KDD, pp. 5225–5236 (2023)
Xia, W., Liu, W., Liu, Y., Tang, R.: Balancing utility and exposure fairness for integrated ranking with reinforcement learning. In: Proceedings of CIKM (2022)
Xie, R., Zhang, S., Wang, R., Xia, F., Lin, L.: Hierarchical reinforcement learning for integrated recommendation. In: Proceedings of AAAI (2021)
Yan, J., Xu, Z., Tiwana, B., Chatterjee, S.: Ads allocation in feed via constrained optimization. In: Proceedings of KDD (2020)
Zehlike, M., et al.: Fa* IR: a fair top-k ranking algorithm. In: Proceedings of CIKM (2017)
Acknowledgements
The Shanghai Jiao Tong University team is partially supported by National Natural Science Foundation of China (62177033). The work is also sponsored by Huawei Innovation Research Program. We thank MindSpore [1] for the partial support of this work.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Liu, Y. et al. (2024). Multi-sourced Integrated Ranking with Exposure Fairness. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_17
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DOI: https://doi.org/10.1007/978-981-97-2262-4_17
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