首先每个item有两个表达e和q
把e按照session 中item的顺序放到一起，构成S矩阵
所谓的Feature-level: 以S为输入，经过 Gated Linear Unit（GLU），输出S^F （给每个item的特征向量里的特征加权）
所谓的Instance-level：以S^F为输入，经过GLU，输出S^I （给每个item加权）
对S^I进行aggregation操作，使得S矩阵最终变成一个向量S^avg
最后，用户表达uq, S^avg q, eq 共同作用得到最后评分。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Hierarchical Gating Networks for Sequential Recommendation | |||||||||||
| 文献作者 | Chen Ma | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| 组织set（session）中的item embeddings | |||||||||||
| 摘要描述 | |||||||||||
| The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation. | |||||||||||
