  Representation learning is essential for deep-neural-network-based
recommender systems to capture user preferences and item features within
fixed-dimensional user and item vectors. Unlike existing representation
learning methods that either treat each user preference and item feature
uniformly or categorize them into discrete clusters, we argue that in the real
world, user preferences and item features are naturally expressed and organized
in a hierarchical manner, leading to a new direction for representation
learning. In this paper, we introduce a novel matryoshka representation
learning method for recommendation (MRL4Rec), by which we restructure user and
item vectors into matryoshka representations with incrementally dimensional and
overlapping vector spaces to explicitly represent user preferences and item
features at different hierarchical levels. We theoretically establish that
constructing training triplets specific to each level is pivotal in
guaranteeing accurate matryoshka representation learning. Subsequently, we
propose the matryoshka negative sampling mechanism to construct training
triplets, which further ensures the effectiveness of the matryoshka
representation learning in capturing hierarchical user preferences and item
features. The experiments demonstrate that MRL4Rec can consistently and
substantially outperform a number of state-of-the-art competitors on several
real-life datasets. Our code is publicly available at
https://github.com/Riwei-HEU/MRL.
