  Deep Candidate Generation plays an important role in large-scale recommender
systems. It takes user history behaviors as inputs and learns user and item
latent embeddings for candidate generation. In the literature, conventional
methods suffer from two problems. First, a user has multiple embeddings to
reflect various interests, and such number is fixed. However, taking into
account different levels of user activeness, a fixed number of interest
embeddings is sub-optimal. For example, for less active users, they may need
fewer embeddings to represent their interests compared to active users. Second,
the negative samples are often generated by strategies with unobserved
supervision, and similar items could have different labels. Such a problem is
termed as class collision. In this paper, we aim to advance the typical
two-tower DNN candidate generation model. Specifically, an Adaptive Interest
Selection Layer is designed to learn the number of user embeddings adaptively
in an end-to-end way, according to the level of their activeness. Furthermore,
we propose a Prototypical Contrastive Learning Module to tackle the class
collision problem introduced by negative sampling. Extensive experimental
evaluations show that the proposed scheme remarkably outperforms competitive
baselines on multiple benchmarks.
