Next basket recommendation V.S. Next next recommendation
前者考虑下一个session中的items，后者考虑当前session中的items
本文花了大篇幅介绍了结合sequental information和general tastes的必要性，以及一些结合方法的弊端，即llnearly combine存在独立性假设（sequental information和general tastes是分开影响最终的用户行为的，其实不然）。
本文的做法很简单：
（1）融合最后一个session中的items，形成represenation
（2）融合（1）和user representation
（3）利用（2）和item representation做最后的推荐
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Learning Hierarchical Representation Model for Next Basket Recommendation | |||||||||||
| 文献作者 | Pengfei Wang | ||||||||||
| 文献发表年限 | 2015 | ||||||||||
| 文献关键字 | |||||||||||
| HRM; aggregation operation; next basket | |||||||||||
| 摘要描述 | |||||||||||
| Next basket recommendation is a crucial task in market basket analysis. Given a user’s purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buy- ing one item leads to buying another next), as well as account for users’ general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users’ next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users’ general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data. | |||||||||||
