local encoder的好处是自适应的专注于session中对于捕获用户主要兴趣的一些重要的item。 NARM Model image.png 从前面的介绍可以看出 global encoder是对于整个序列行为的汇总, local encoder则是用来捕获session序列中对于捕获用户兴趣的比较重要的item。我们推测,序列行为的表示可能为捕获当前会话中用户的主要目的提供有用...
• Motivation (Why)：第一篇提出将 RNN 应用到 session-based recommendation 的论文。• Main Idea (What)：一个 session 中点击 item 的行为看做一个序列，用 GRU 来刻画。• How：模型（GRU4REC）架构 模型输入： session 中的点击序列，, 1 ≤ r < n，通过 one hot encoding 编码，通过 ...
Neural Attentive Session-based Recommendation 介绍 作者提出之前的工作只考虑了用户的序列表现,但是对用户的主要目的并没有明显地强调,因此作者提出Neural Attentive Recommendation Machine(NARM) 方法 NARM的整体框架如下: 编码器方面含有global encoder和local encoder。 global... 查看原文 GNN:Session-based ...
7. Neural Attentive Session-based Recommendation. (CIKM 2017:未公布论文) next basket recommendation也可以看做序列数据,之后再做整理 8. Next Basket Recommendation with Neural Networks (Recsys 2015) 9. A Dynamic Recurrent Model for Next Basket Recommendation (SIGIR 2016) 参考文献 [1] Sarwar, Badrul...
Attentive Capsule Graph Neural Networks forSession-Based Recommendation To solve these problems, we propose a novel attentive capsule graph neural network for session-based recommendation (ACGNN) to mine more profound user ... Y Chen,Y Tang - International Conference on Knowledge Science 被引量: 0...
Therefore, we propose a novel high-order attentive graph neural network (HA-GNN) model for session-based recommendations. In the proposed method, first, we model sessions as graph-structured data. Then, we use the self-attention mechanism to capture the dependencies between items. Next, we use...
传统的两类推荐方法——基于内容的推荐算法和协同过滤推荐算法(model-based、memory-based)在刻画序列数据中存在缺陷:每个item相互独立,不能建模session中item的连续偏好信息。 二、传统的解决方法 1. item-to-item recommendation approach (Sarwar et al.,2001; Linden et al., 2003) : 采用session中item间的相似...
Session-based recommendation (SBR) predicts the next interaction of users based on their clicked items in a session. Previous studies have shown that hypergraphs are superior in capturing complex item transitions which contribute to SBR performance. However, existing hypergraph-based methods fail to mod...
③ Session based recommendation 手机端(如TikTok等)的存储空间不够,不能存储大量的用户过往信息 与Sequential recommendation不同,Session based recommendation中同一用户的后续会话是独立处理的,因为每个会话中用户的行为只表现出基于会话的特征 ④ Bundle recommendation 推荐商品组合供用户消费 ⑤ Cross-domain recommendati...
Category attentive graph neural networks for session-based recommendation Session-based recommendation is a challenging field in the research network-based behavior modeling, mainly due to the complex transfer of user interests b... W Qin,X Fu,X Yang,... 被引量: 0发表: 2022年 Hypergraph Neu...