LIFE: Lifetime-Aware Fair Exposure for Time-Limited Items in Recommendation Systems
Resumo
Recommendation systems play an important role by predicting users’ preferences while allocating visibility and economic opportunity to item providers. This becomes especially challenging when items have limited lifetimes (e.g., news articles, political campaigns, perishable goods, and seasonal products), because time-sensitive attention can be disproportionately captured by a small subset of items due to position bias and popularity effects. Building on the notion of fair exposure, we propose LIFE , a multi-objective post-processing approach that adjusts users’ top-k lists to balance user relevance, provider fairness, and item urgency, proportionally estimating quality over a time window while prioritizing items closer to the end of their lifetimes.
Palavras-chave:
Fairness, Recommender Systems, Reranking, Time-Limited Items
Referências
Amanatidis, G., Aziz, H., Birmpas, G., Filos-Ratsikas, A., Li, B., Moulin, H., Voudouris, A. A., and Wu, X. (2023). Fair division of indivisible goods: Recent progress and open questions. Artificial Intelligence, 322:103965.
Arnsperger, C. (1994). Envy-freeness and distributive justice. Journal of Economic Surveys, 8(2):155–186.
Bae, H.-K., Ahn, J., Lee, D., and Kim, S.-W. (2023). Lancer: A lifetime-aware news recommender system. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4141–4148.
Ben-Porat, O. and Torkan, R. (2023). Learning with exposure constraints in recommendation systems. In Proceedings of the ACM Web Conference 2023, pages 3456–3466.
Biega, A. J., Gummadi, K. P., and Weikum, G. (2018). Equity of attention: Amortizing individual fairness in rankings. CoRR, abs/1805.01788.
Brams, S. J. and Taylor, A. D. (1995). An envy-free cake division protocol. The American Mathematical Monthly, 102(1):9–18.
Collins, T., Beel, J., and Carey, S. (2018). A study of position bias in digital library recommender systems. In Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL ’18), pages 335–344, New York, NY, USA. Association for Computing Machinery.
de Lourdes M. Silva, M., Chaves, I., Mendonça, A. L., Neto, E. D., and Machado, J. (2025). Twix: Balancing fairness and utility in item exposure for recommendation systems. In Anais do XL Simpósio Brasileiro de Bancos de Dados, pages 427–440, Porto Alegre, RS, Brasil. SBC.
Do, V., Corbett-Davies, S., Atif, J., and Usunier, N. (2021). Two-sided fairness in rankings via lorenz dominance. Advances in Neural Information Processing Systems, 34:8596–8608.
Farhadi, A., Hajiaghayi, M., Latifian, M., Seddighin, M., and Yami, H. (2021). Almost envy-freeness, envy-rank, and nash social welfare matchings. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 5355–5362.
Guo, H., Sun, Z., Wang, D., Wei, T., Li, J., and Zhang, J. (2025). Enhancing new-item fairness in dynamic recommender systems. In Proceedings of the 48th International ACM SIGIR conference on research and development in Information Retrieval, pages 1707–1716.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19.
Heuss, M., Sarvi, F., and de Rijke, M. (2022). Fairness of exposure in light of incomplete exposure estimation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 759–769.
Joachims, T., Granka, L., Pan, B., Hembrooke, H., and Gay, G. (2017). Accurately interpreting clickthrough data as implicit feedback. In Acm Sigir Forum, volume 51, pages 4–11. Acm New York, NY, USA.
Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30–37.
Li, Y., Chen, H., Fu, Z., Ge, Y., and Zhang, Y. (2021). User-oriented fairness in recommendation. In Proceedings of the web conference 2021, pages 624–632.
Macedo, A. and Marinho, L. (2014). Event recommendation in event-based social networks. Proc. of Int. Work. on Social Personalization.
Morik, M., Singh, A., Hong, J., and Joachims, T. (2020). Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 429–438.
Rawls, J. (2017). A theory of justice. In Applied ethics, pages 21–29. Routledge.
Raza, S. and Ding, C. (2022). News recommender system: a review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55(1):749–800.
Singh, A. and Joachims, T. (2018). Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2219–2228.
Usunier, N., Do, V., and Dohmatob, E. (2022). Fast online ranking with fairness of exposure. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 2157–2167.
Wang, J., Ma, W., Li, J., Lu, H., Zhang, M., Li, B., Liu, Y., Jiang, P., and Ma, S. (2022). Make fairness more fair: Fair item utility estimation and exposure re-distribution. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pages 1868–1877.
Wang, Y., Ma, W., Zhang, M., Liu, Y., and Ma, S. (2023). A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 41(3):1–43.
Wu, Y., Cao, J., and Xu, G. (2023). Faster: A dynamic fairness-assurance strategy for session-based recommender systems. ACM Transactions on Information Systems (TOIS), 42(1):1–26.
Wu, Y., Cao, J., Xu, G., and Tan, Y. (2021). Tfrom: A two-sided fairness-aware recommendation model for both customers and providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 1013–1022, New York, NY, USA. Association for Computing Machinery.
Zhang, F., Liu, Q., and Zeng, A. (2017). Timeliness in recommender systems. Expert Systems with Applications, 85:270–278.
Arnsperger, C. (1994). Envy-freeness and distributive justice. Journal of Economic Surveys, 8(2):155–186.
Bae, H.-K., Ahn, J., Lee, D., and Kim, S.-W. (2023). Lancer: A lifetime-aware news recommender system. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4141–4148.
Ben-Porat, O. and Torkan, R. (2023). Learning with exposure constraints in recommendation systems. In Proceedings of the ACM Web Conference 2023, pages 3456–3466.
Biega, A. J., Gummadi, K. P., and Weikum, G. (2018). Equity of attention: Amortizing individual fairness in rankings. CoRR, abs/1805.01788.
Brams, S. J. and Taylor, A. D. (1995). An envy-free cake division protocol. The American Mathematical Monthly, 102(1):9–18.
Collins, T., Beel, J., and Carey, S. (2018). A study of position bias in digital library recommender systems. In Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL ’18), pages 335–344, New York, NY, USA. Association for Computing Machinery.
de Lourdes M. Silva, M., Chaves, I., Mendonça, A. L., Neto, E. D., and Machado, J. (2025). Twix: Balancing fairness and utility in item exposure for recommendation systems. In Anais do XL Simpósio Brasileiro de Bancos de Dados, pages 427–440, Porto Alegre, RS, Brasil. SBC.
Do, V., Corbett-Davies, S., Atif, J., and Usunier, N. (2021). Two-sided fairness in rankings via lorenz dominance. Advances in Neural Information Processing Systems, 34:8596–8608.
Farhadi, A., Hajiaghayi, M., Latifian, M., Seddighin, M., and Yami, H. (2021). Almost envy-freeness, envy-rank, and nash social welfare matchings. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 5355–5362.
Guo, H., Sun, Z., Wang, D., Wei, T., Li, J., and Zhang, J. (2025). Enhancing new-item fairness in dynamic recommender systems. In Proceedings of the 48th International ACM SIGIR conference on research and development in Information Retrieval, pages 1707–1716.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19.
Heuss, M., Sarvi, F., and de Rijke, M. (2022). Fairness of exposure in light of incomplete exposure estimation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 759–769.
Joachims, T., Granka, L., Pan, B., Hembrooke, H., and Gay, G. (2017). Accurately interpreting clickthrough data as implicit feedback. In Acm Sigir Forum, volume 51, pages 4–11. Acm New York, NY, USA.
Koren, Y., Bell, R., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30–37.
Li, Y., Chen, H., Fu, Z., Ge, Y., and Zhang, Y. (2021). User-oriented fairness in recommendation. In Proceedings of the web conference 2021, pages 624–632.
Macedo, A. and Marinho, L. (2014). Event recommendation in event-based social networks. Proc. of Int. Work. on Social Personalization.
Morik, M., Singh, A., Hong, J., and Joachims, T. (2020). Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 429–438.
Rawls, J. (2017). A theory of justice. In Applied ethics, pages 21–29. Routledge.
Raza, S. and Ding, C. (2022). News recommender system: a review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55(1):749–800.
Singh, A. and Joachims, T. (2018). Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2219–2228.
Usunier, N., Do, V., and Dohmatob, E. (2022). Fast online ranking with fairness of exposure. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 2157–2167.
Wang, J., Ma, W., Li, J., Lu, H., Zhang, M., Li, B., Liu, Y., Jiang, P., and Ma, S. (2022). Make fairness more fair: Fair item utility estimation and exposure re-distribution. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pages 1868–1877.
Wang, Y., Ma, W., Zhang, M., Liu, Y., and Ma, S. (2023). A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 41(3):1–43.
Wu, Y., Cao, J., and Xu, G. (2023). Faster: A dynamic fairness-assurance strategy for session-based recommender systems. ACM Transactions on Information Systems (TOIS), 42(1):1–26.
Wu, Y., Cao, J., Xu, G., and Tan, Y. (2021). Tfrom: A two-sided fairness-aware recommendation model for both customers and providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 1013–1022, New York, NY, USA. Association for Computing Machinery.
Zhang, F., Liu, Q., and Zeng, A. (2017). Timeliness in recommender systems. Expert Systems with Applications, 85:270–278.
Publicado
08/09/2026
Como Citar
CASTRO, Maria Ianne B.; M. SILVA, Maria de Lourdes; CHAVES, Iago C.; MACHADO, Javam C..
LIFE: Lifetime-Aware Fair Exposure for Time-Limited Items in Recommendation Systems. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 387-400.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249225.
