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.

翻译：物品表示在推荐系统中具有重要意义，涵盖新闻、零售和视频等领域。检索和排序模型利用物品表示基于用户行为来捕获用户与物品之间的关系。现有的表示学习方法主要侧重于优化基于物品的机制，如注意力机制和序列建模。然而，这些方法缺乏在学习到的物品表示中直接反映用户兴趣的建模机制。因此，这些方法在间接捕获用户兴趣方面可能效果欠佳。为解决这一挑战，我们提出了一种新颖的兴趣感知胶囊网络（IaCN）推荐模型，这是一种模型无关的框架，能够直接学习面向兴趣的物品表示。IaCN作为辅助任务，实现了基于物品和基于兴趣表示的联合学习。该框架采用现有推荐模型，无需进行大量重新设计。我们在基准数据集上对所提方法进行了评估，探索了涉及不同深度神经网络、行为序列长度以及面向兴趣物品表示联合学习比例的各种场景。实验结果表明，该框架在多种推荐模型中均实现了显著的性能提升，验证了我们方法的有效性。