将物品频率信息融入Transformer，提升购物篮推荐准确率
SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation
- 用自注意力+频率信息增强Transformer架构
- Recall@10指标提升8%，超越所有基线方法
- 适合需要高精度购物篮推荐的电商场景
基于Transformer的方法（如BERT4Rec和SASRec）在下一步物品推荐（NIR）任务中表现优异。然而，将其应用于涉及高度重复交互的下一步购物篮推荐（NBR）任务时面临挑战，因购物篮中可能的物品组合数量庞大。此外，基于频率的方法（如TIFU-KNN和UP-CF）在NBR任务中仍表现出色，常优于深度学习模型。本文提出SAFERec，一种新颖的NBR算法，通过引入物品频率信息增强Transformer架构，从而提升其在NBR任务中的适用性。在多个数据集上的大量实验表明，SAFERec超越所有基线模型，尤其在Recall@10上实现8%的提升。
原文摘要 · Abstract (English)
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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