CausalRec: A CausalBoost Attention Model for Sequential Recommendation

TL;DR

CausalRec introduces a novel causal attention framework for sequential recommendation, improving accuracy by modeling user behavior causality and outperforming state-of-the-art methods on real datasets.

Contribution

It is the first to incorporate causality into attention mechanisms for sequential recommendation, using a causal discovery block and CausalBooster to enhance recommendation quality.

Findings

CausalRec outperforms existing methods with 7.21% higher Hit Rate.

CausalRec achieves 8.65% better NDCG scores.

The model demonstrates the effectiveness of causality in recommendation accuracy.

Abstract

Recent advances in correlation-based sequential recommendation systems have demonstrated substantial success. Specifically, the attention-based model outperforms other RNN-based and Markov chains-based models by capturing both short- and long-term dependencies more effectively. However, solely focusing on item co-occurrences overlooks the underlying motivations behind user behaviors, leading to spurious correlations and potentially inaccurate recommendations. To address this limitation, we present a novel framework that integrates causal attention for sequential recommendation, CausalRec. It incorporates a causal discovery block and a CausalBooster. The causal discovery block learns the causal graph in user behavior sequences, and we provide a theory to guarantee the identifiability of the learned causal graph. The CausalBooster utilizes the discovered causal graph to refine the attention mechanism, prioritizing behaviors with causal significance. Experimental evaluations on real-world datasets indicate that CausalRec outperforms several state-of-the-art methods, with average improvements of 7.21% in Hit Rate (HR) and 8.65% in Normalized Discounted Cumulative Gain (NDCG). To the best of our knowledge, this is the first model to incorporate causality through the attention mechanism in sequential recommendation, demonstrating the value of causality in generating more accurate and reliable recommendations.