关联项目增强的多兴趣序列推荐方法

现有基于多兴趣框架的序列推荐方法仅从用户近期交互序列中学习得到用户多兴趣表示，忽略了数据集中项目间的关联信息。针对这一问题，提出了一种关联项目增强的多兴趣序列推荐方法IAMIRec（item associations aware multi-interest sequential recommendation method）。首先通过数据集中用户交互序列计算得到项目关联集合和对应的项目关联矩阵，然后根据项目关联矩阵通过多头自注意力机制建模用户的近期交互序列，最后使用多兴趣框架学习得到用户的多个兴趣向量并进行top-N推荐。在三个数据集上对该方法进行了测试与分析，IAMIRec在recall、NDCG（normalized discounted cumulative gain）和hit rate指标上的表现均优于相关方法。实验结果说明 IAMIRec可以实现更优的推荐性能，也表明引入项目关联信息可以有效增强用户的多兴趣表示。

Item associations aware multi-interest sequential recommendation method

Existing sequential recommendation methods based on multi-interest frameworks only learn the multi-interest representations from users'' recent interaction sequences, ignoring the association information between items in the dataset. To address this problem, this paper proposed an item associations aware multi-interest sequential recommendation method(IAMIRec). Firstly, this method obtained the item association set and matrix by calculating the user''s interaction sequence in the dataset, then modeled the user''s recent interaction sequence by the association martix and the multi-head self-attention mechanism, and used the multi-interest framework to model the user''s multiple interest vectors. It used the obtained multiple interest vectors to the top-N recommendation for users. This paper tested and analyzed the model on three datasets, IAMIRec outperformed related methods on recall, NDCG, and hit rate metrics. The results show that IAMIRec can achieve better recommendation performance, and also show that it can effectively enhance the multi-interest representation of users through item associations information.