SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation

• URL: http://arxiv.org/abs/2412.14302v2
• Date: Fri, 20 Dec 2024 07:56:22 GMT
• Title: SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation
• Authors: Oleg Lashinin, Denis Krasilnikov, Aleksandr Milogradskii, Marina Ananyeva,
• Abstract summary: Transformer-based approaches such as BERT4Rec and SASRec demonstrate strong performance in Next Item Recommendation (NIR) tasks. Applying these architectures to Next-Basket Recommendation (NBR) tasks is challenging due to the vast number of possible item combinations in a basket. This paper introduces SAFERec, a novel algorithm for NBR that enhances transformer-based architectures from NIR by item frequency information.
• Score: 41.94295877935867
• License:
• Abstract: Transformer-based approaches such as BERT4Rec and SASRec demonstrate strong performance in Next Item Recommendation (NIR) tasks. However, applying these architectures to Next-Basket Recommendation (NBR) tasks, which often involve highly repetitive interactions, is challenging due to the vast number of possible item combinations in a basket. Moreover, frequency-based methods such as TIFU-KNN and UP-CF still demonstrate strong performance in NBR tasks, frequently outperforming deep-learning approaches. This paper introduces SAFERec, a novel algorithm for NBR that enhances transformer-based architectures from NIR by incorporating item frequency information, consequently improving their applicability to NBR tasks. Extensive experiments on multiple datasets show that SAFERec outperforms all other baselines, specifically achieving an 8\% improvement in Recall@10.

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