LIFE: Lifetime-Aware Fair Exposure for Time-Limited Items in Recommendation Systems - Castro, Maria Ianne B.; M. Silva, Maria de Lourdes; Chaves, Iago C.; Machado, Javam C. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
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.