A Model-Agnostic Framework for Recommendation via Interest-aware Item
  Embeddings
Amit Kumar Jaiswal Yu Xiong
Item representation holds significant importance in recommendation systems,
which encompasses domains such as news, retail, and videos. Retrieval and
ranking models utilise item representation to capture the user-item
relationship based on user behaviours. While existing representation learning
methods primarily focus on optimising item-based mechanisms, such as attention
and sequential modelling. However, these methods lack a modelling mechanism to
directly reflect user interests within the learned item representations.
Consequently, these methods may be less effective in capturing user interests
indirectly. To address this challenge, we propose a novel Interest-aware
Capsule network (IaCN) recommendation model, a model-agnostic framework that
directly learns interest-oriented item representations. IaCN serves as an
auxiliary task, enabling the joint learning of both item-based and
interest-based representations. This framework adopts existing recommendation
models without requiring substantial redesign. We evaluate the proposed
approach on benchmark datasets, exploring various scenarios involving different
deep neural networks, behaviour sequence lengths, and joint learning ratios of
interest-oriented item representations. Experimental results demonstrate
significant performance enhancements across diverse recommendation models,
validating the effectiveness of our approach.