本质上不是传统意义上的bundle recommendation，因为最后并不是给出bundle的，也没有对bundle recommendations的性能做出评估。
但本文的主要贡献在于提出了一个aggregation operation的方法。如何利用bundle中的item embeddings构造bundle embedding。做法：加权。而这个权值是利用user embedding和additional item embedding得出的（每个item 有两个embedding）。
最后利用BPR的思想，分别有item-wise的loss和bundle-wise的loss，最后利用MLP将两者联系在一起，成为最后的Loss。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network | |||||||||||
| 文献作者 | Zibin Zheng; Xiangnan He | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| attention nn; ijcai；aggregation operation | |||||||||||
| 摘要描述 | |||||||||||
| Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle. In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle’s representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM. | |||||||||||
