  Existing research efforts for multi-interest candidate matching in
recommender systems mainly focus on improving model architecture or
incorporating additional information, neglecting the importance of training
schemes. This work revisits the training framework and uncovers two major
problems hindering the expressiveness of learned multi-interest
representations. First, the current training objective (i.e., uniformly sampled
softmax) fails to effectively train discriminative representations in a
multi-interest learning scenario due to the severe increase in easy negative
samples. Second, a routing collapse problem is observed where each learned
interest may collapse to express information only from a single item, resulting
in information loss. To address these issues, we propose the REMI framework,
consisting of an Interest-aware Hard Negative mining strategy (IHN) and a
Routing Regularization (RR) method. IHN emphasizes interest-aware hard
negatives by proposing an ideal sampling distribution and developing a
Monte-Carlo strategy for efficient approximation. RR prevents routing collapse
by introducing a novel regularization term on the item-to-interest routing
matrices. These two components enhance the learned multi-interest
representations from both the optimization objective and the composition
information. REMI is a general framework that can be readily applied to various
existing multi-interest candidate matching methods. Experiments on three
real-world datasets show our method can significantly improve state-of-the-art
methods with easy implementation and negligible computational overhead. The
source code will be released.
