The paper "Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding" by Jiaxi Tang and Ke Wang introduces a novel model called Convolutional Sequence Embedding Recommendation Model (Caser) for top-N sequential recommendation. The model aims to predict the next N items a user is likely to interact with in the near future by capturing both general preferences and sequential patterns. Caser represents a sequence of recent items as an "image" in the time and latent spaces, using convolutional filters to learn sequential patterns as local features of this image. This approach allows Caser to model both point-level and union-level sequential patterns, as well as skip behaviors, in a unified framework. Experiments on public datasets demonstrate that Caser outperforms state-of-the-art methods on various evaluation metrics, including Precision@N, Recall@N, and Mean Average Precision (MAP). The paper also discusses the contributions of Caser, its architecture, training process, and performance comparison with existing models.The paper "Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding" by Jiaxi Tang and Ke Wang introduces a novel model called Convolutional Sequence Embedding Recommendation Model (Caser) for top-N sequential recommendation. The model aims to predict the next N items a user is likely to interact with in the near future by capturing both general preferences and sequential patterns. Caser represents a sequence of recent items as an "image" in the time and latent spaces, using convolutional filters to learn sequential patterns as local features of this image. This approach allows Caser to model both point-level and union-level sequential patterns, as well as skip behaviors, in a unified framework. Experiments on public datasets demonstrate that Caser outperforms state-of-the-art methods on various evaluation metrics, including Precision@N, Recall@N, and Mean Average Precision (MAP). The paper also discusses the contributions of Caser, its architecture, training process, and performance comparison with existing models.