A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings

• URL: http://arxiv.org/abs/2308.09202v1
• Date: Thu, 17 Aug 2023 22:40:59 GMT
• Title: A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings
• Authors: Amit Kumar Jaiswal, Yu Xiong
• Abstract summary: Interest-aware Capsule network (IaCN) is 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. We evaluate the proposed approach on benchmark datasets, exploring various scenarios involving different deep neural networks.
• Score: 4.989653738257287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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