Cooperative Retriever and Ranker in Deep Recommenders

Deep recommender systems jointly leverage the retrieval and ranking operations to generate the recommendation result. The retriever targets selecting 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 identify the best items out of the retrieved candidates with high precision. However, the retriever and ranker are usually trained in poorly-cooperative ways, leading to limited recommendation performances when working as an entirety. In this work, we propose a novel DRS training framework CoRR(short for Cooperative Retriever and Ranker), where the retriever and ranker can be mutually reinforced. On one hand, the retriever is learned from recommendation data and the ranker via knowledge distillation; knowing that the ranker is more precise, the knowledge distillation may provide extra weak-supervision signals for the improvement of retrieval quality. On the other hand, the ranker is trained by learning to discriminate the truth positive items from hard negative candidates sampled from the retriever. With the iteration going on, the ranker may become more precise, which in return gives rise to informative training signals for the retriever; meanwhile, with the improvement of retriever, harder negative candidates can be sampled, which contributes to a higher discriminative capability of the ranker. To facilitate the effective conduct of CoRR, an asymptotic-unbiased approximation of KL divergence is introduced for the knowledge distillation over sampled items; besides, a scalable and adaptive strategy is developed to efficiently sample from the retriever. Comprehensive experimental studies are performed over four large-scale benchmark datasets, where CoRR improves the overall recommendation quality resulting from the cooperation between retriever and ranker.