A Dual Augmented Two-tower Model for Online Large-scale Recommendation

Abstract

Many modern recommender systems have a very large corpus, and a common industrial recipe for handling large-scale retrieval is to learn query and item representations from their content features with the two-tower model. However, the model suffers from lack of information interaction between the two towers. Besides, imbalanced category data also hinders the model performance. In this paper, we propose a novel model named Dual Augmented Two-tower Model (DAT), which integrates a novel Adaptive-Mimic Mechanism (AMM) and a Category Alignment Loss (CAL). Our AMM customizes an augmented vector for each query and item to mitigate the lack of information interaction. Moreover, our CAL can further improve performance by aligning item representation of uneven categories. Offline experiments on large-scale datasets are conducted to show the superior performance of DAT. More-over, online A/B testings confirm that DAT can lead to improved recommendation quality for industrial applications.

Tasks

Reproductions