An improved sequential recommendation model based on spatial self-attention mechanism and meta learning.

Sequential recommendation systems in cold-start scenarios aim to provide recommendations as accurately as possible for users with sparse behavior, which is a challenging issue in this field. Recently, meta-learning algorithms have been introduced into the cold-start recommendations and have obtained some better results. However, most of these meta-learning-based methods require auxiliary information or knowledge from other fields, and do not model the third-order relationship between users and their sequential interactive items, which could result in models lacking the ability to capture dynamic transitions of user preferences. In addition, the traditional meta-learning-based methods ignore the diversity of user preferences, which limits the performance improvement in sequential recommendation systems in the cold-start scenarios. To address the above problems, a new sequential recommendation model is proposed for cold-start scenarios which combines meta-learning and attention mechanism. In the proposed model, a third-order interaction modeling paradigm of "Item-User-Item" (IUI) is proposed firstly to obtain dynamic transitions of user preferences in the item space. Then, an embedding strategy is used to embed the user behavior sequences and transition information of user preferences from the training task into a spatial self-attentive (SS) recommendation model. Moreover, a meta-learning-based parameter training approach is presented, where a task processor (TP) is designed to improve the universality of parameter initialization. At last, some experiments are conducted on three real-world datasets, and the evaluation metrics HR@10 and NDCG@10 of the proposed model improve by about 5.1% and 5.6% compared with the baseline, respectively. The experimental results show that the proposed model in this paper achieves the best recommendation results in comparison with existing models. [ABSTRACT FROM AUTHOR]