Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).

中文翻译：

---

用于顺序推荐的深度双 LSTM 网络

近年来，结合深度学习和推荐系统来捕捉用户偏好或项目交互随时间演变的方法激增。然而，最相关的工作只考虑项目之间的顺序相似性，而忽略了项目内容特征信息和交互项目对下一个项目的影响差异。本文将深度双向长短期记忆（LSTM）和自注意力机制引入顺序推荐器，同时融合项目序列和内容的信息。具体来说，我们处理三管齐下的攻击问题：改进的项目嵌入、权重更新和深度双向 LSTM 偏好学习。首先，用户-项目序列通过 Item2vec 嵌入到低维项目向量空间表示中，并且为每个嵌入的项目向量连接类标签向量。其次，嵌入的item向量通过self-attention机制学习每个item的不同影响权重，实现item的感知；然后将嵌入的项目向量和相应的权重输入双向 LSTM 模型以学习用户偏好向量。最后，评估偏好向量空间中的顶部相似项以生成用户的推荐列表。通过进行全面的实验，我们证明我们的模型在 Recall@20 和 Mean Reciprocal Rank (MRR@20) 上优于传统的推荐算法。然后将嵌入的项目向量和相应的权重输入双向 LSTM 模型以学习用户偏好向量。最后，评估偏好向量空间中的顶部相似项以生成用户的推荐列表。通过进行全面的实验，我们证明我们的模型在 Recall@20 和 Mean Reciprocal Rank (MRR@20) 上优于传统的推荐算法。然后将嵌入的项目向量和相应的权重输入双向 LSTM 模型以学习用户偏好向量。最后，评估偏好向量空间中的顶部相似项以生成用户的推荐列表。通过进行全面的实验，我们证明我们的模型在 Recall@20 和 Mean Reciprocal Rank (MRR@20) 上优于传统的推荐算法。