Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer

TL;DR

This paper introduces MT4SR, a multi-relational transformer that models auxiliary item relationships in sequential recommendation, improving cold start handling and outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a novel multi-relational transformer with specialized modules to incorporate auxiliary item relationships into sequential recommendation models.

Findings

MT4SR outperforms state-of-the-art methods on four benchmark datasets.

The model effectively alleviates the cold start problem.

Incorporating auxiliary relationships improves recommendation accuracy.

Abstract

Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item transitions observed in sequences, which can be viewed as an item relationship. However, there are multiple auxiliary item relationships, e.g., items from similar brands and with similar contents in real-world scenarios. Auxiliary item relationships describe item-item affinities in multiple different semantics and alleviate the long-lasting cold start problem in the recommendation. However, it remains a significant challenge to model auxiliary item relationships in SR. To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR). Specifically, we propose a novel self-attention module, which incorporates arbitrary item relationships and weights item relationships accordingly. Second, we regularize intra-sequence item relationships with a novel regularization module to supervise attentions computations. Third, for inter-sequence item relationship pairs, we introduce a novel inter-sequence related items modeling module. Finally, we conduct experiments on four benchmark datasets and demonstrate the effectiveness of MT4SR over state-of-the-art methods and the improvements on the cold start problem. The code is available at https://github.com/zfan20/MT4SR.