A Novel User Representation Paradigm for Making Personalized Candidate Retrieval

Candidate retrieval is a crucial part in recommendation system, where quality candidates need to be selected in realtime for user's recommendation request. Conventional methods would make use of feature similarity directly for highly scalable retrieval, yet their retrieval quality can be limited due to inferior user interest modeling. In contrast, deep learning-based recommenders are precise in modeling user interest, but they are difficult to be scaled for efficient candidate retrieval. In this work, a novel paradigm Synthonet is proposed for both precise and scalable candidate retrieval. With Synthonet, user is represented as a compact vector known as retrieval key. By developing an Actor-Critic learning framework, the generation of retrieval key is optimally conducted, such that the similarity between retrieval key and item's representation will accurately reflect user's interest towards the corresponding item. Consequently, quality candidates can be acquired in realtime on top of highly efficient similarity search methods. Comprehensive empirical studies are carried out for the verification of our proposed methods, where consistent and remarkable improvements are achieved over a series of competitive baselines, including representative variations on metric learning.