Latest Research Papers
2024-12-18
arXiv
SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation
SAFERec, a new algorithm for Next-Basket Recommendation, enhances transformer-based models by incorporating item frequency information, improving their performance on NBR tasks. Experiments show SAFERec outperforms other baselines, with an 8% improvement in Recall@10.
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.