论文标题
深度推荐人的合作猎犬和排名
Cooperative Retriever and Ranker in Deep Recommenders
论文作者
论文摘要
深度推荐系统（DRS）在现代网络服务中强烈应用。为了处理大量的Web内容，DRS采用了两个阶段的工作流程：检索和排名，以产生其建议结果。猎犬旨在从效率高的整个项目中选择一小部分相关候选人；尽管通常更精确但耗时的排名者应该进一步完善从检索到的候选人中的最佳项目。传统上，这两个组件是独立或在简单的级联管道中进行培训的，这很容易获得协作效果不佳。尽管一些最新的作品建议共同培训猎犬和排名者，但仍然存在许多严重的局限性：培训与推理之间的项目分配变化，假阴性和排名顺序的不对对准。因此，仍然是探索猎犬和Ranker之间的有效合作。
Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.
