| dc.description.abstract | In e-commerce, the importance of recommendation systems is increasing day by day. However, in traditional session-based or sequence-based recommendation modes, previous items are usually used to recommend the possible next item. This approach faces a significant challenge as people tend to repeat purchases. When users like a certain item, they are likely to choose the same item again, resulting in the majority of items recommended by the recommendation system having already appeared in previous sequences. However, this paper argues that an attractive recommendation system should recommend products to users that they don′t know but might like, and once they are aware of such products, they may choose to purchase them. Therefore, when preparing the data, this paper will delete all the target items that have previously appeared in the sequence based on each user′s target item, ensuring that users are unaware of the item. The model proposed in this paper will then predict the target item under this circumstance. This paper designs a deep network architecture based on the concept of the subsequent item of adjacent items in the sequence, incorporating both global and local information to achieve the goal of successfully predicting the target item. Through experiments with multiple real-world datasets, we conducted extensive testing, and the results show that the deep network architecture proposed in this paper outperforms several state-of-the-art recommendation methods in performance. | en_US |