提出兼顾用户与物品结构的推荐算法，显著提升推荐效果。
Optimal Sequential Recommendations: Exploiting User and Item Structure
- 结合用户与物品双重结构进行推荐决策
- 理论证明优于仅用单一结构的方法
- 适合需要高精度推荐的场景
我们研究一种在线推荐模型，用户在每个时间步被推荐一个物品并给出点赞或点踩反馈。采用潜在变量模型描述用户偏好：用户和物品均被聚类为不同类型。该模型捕捉了用户空间和物品空间的结构，类似于物品-物品和用户-用户协同过滤算法。我们分析类型偏好矩阵各元素独立同分布的情形。主要贡献是提出一种同时利用物品与用户结构的算法，并通过对应的信息论下界证明其近似最优。分析表明，仅使用物品或用户结构（如大多数协同过滤算法）存在次优性。
原文摘要 · Abstract (English)
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. A latent variable model specifies the user preferences: both users and items are clustered into types. The model captures structure in both the item and user spaces, as used by item-item and user-user collaborative filtering algorithms. We study the situation in which the type preference matrix has i.i.d. entries. Our main contribution is an algorithm that simultaneously uses both item and user structures, proved to be near-optimal via corresponding information-theoretic lower bounds. In particular, our analysis highlights the sub-optimality of using only one of item or user structure (as is done in most collaborative filtering algorithms).
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