FORTE：工业推荐系统的特征评估与淘汰生产框架
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
推荐系统特征越加越多却不敢删，这篇论文给出了一套可落地的删特征流程，实测砍掉 43.3% 特征还降了成本，做推荐的同行可以看看。
arXiv 论文 FORTE 针对推荐系统特征下线难题，提出模型无关的生产框架，通过扰动实现支持每日特征评估。它用分组消融评估从低秩特征构建的候选特征集，考虑特征间交互，离线结果对在线 A/B 测试有足够预测性。在搜索相关性模型上，FORTE 选定 48 个特征的下线集合，移除该模型 43.3% 的特征。在大型排序模型上识别 8 个候选特征，验证后使可训练参数减少 29.9%，服务吞吐量提升 19.8-25.5%，年度训练成本节省数十万美元。
FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not regress any metric. In practice, feature deprecation is hard to prioritize as a feature can fail to be removed if it interacts with other features, its importance changes daily, it cannot be evaluated before production models change or data drift occurs, it is in contention for A/B test space with other launches, it causes regressions in an online A/B test, or it is consumed by multiple models that need to coordinate its deprecation. We present FORTE, a model-agnostic production framework for discovering, validating, and removing features. Its efficient perturbation implementation allows daily feature evaluation. To account for interactions, it evaluates candidate feature sets constructed from all the low-rank features through grouped ablation and its offline results are sufficiently predictive of online A/B tests to allow deprecations to proceed to online testing with confidence. Lineage tracing identifies all downstream consumer models to realize the full savings of removing a feature from the training set. We validate FORTE over 2 large-scale recommender systems and find that it identifies deprecable feature sets which do not cause online regressions, while the impact on efficiency depends on model characteristics. On a search-relevance model, FORTE resulted in 3 nested candidate sets of 32, 48, and 59 features. Online guardrails rejected the largest treatment. The 48-feature set was selected for deprecation, removing 43.3% of the model's features. On a large ranking model, FORTE identified 8 candidates for removal. Retraining and A/B testing confirmed no regression. Its launch reduced the trainable parameters by 29.9%, improved serving throughput by 19.8-25.5%, and reduced annual training costs by hundreds of thousands of dollars.
