本文argue一个session中可以不只一个purpose（而大多数的session-based的文章可能都假设为一个purpose），因此本文提出了multiple-purpose based model for session-based recommendation.
本文是通过假设一个session中的items可能分属于不同的purpose，因此会计算每个item属于每个purpose的概率。最后把所有的item组合到一起。
这里的问题：没有把user放进去。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks | |||||||||||
| 文献作者 | Liang hu | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| purpose; session-based; ijcai | |||||||||||
| 摘要描述 | |||||||||||
| A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subset have strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. To address the shortcomings in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item and assign them into the corresponding channels. Moreover, a purpose-specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity. | |||||||||||
