Collaborative recommendation approaches based on nearest-neighbors are still
highly popular today due to their simplicity, their efficiency, and their
ability to produce accurate and personalized recommendations. This chapter
offers a comprehensive survey of neighborhood-based methods for the item
recommendation problem. It presents the main characteristics and benefits of
such methods, describes key design choices for implementing a
neighborhood-based recommender system, and gives practical information on how
to make these choices. A broad range of methods is covered in the chapter,
including traditional algorithms like k-nearest neighbors as well as advanced
approaches based on matrix factorization, sparse coding and random walks.

本文综述了基于最近邻的协同过滤算法，包括传统算法和先进算法如矩阵分解、稀疏编码和随机游走，介绍了设计实现最近邻推荐系统的关键选择和实践信息。

信任邻居：推荐系统基于邻居的综合调查

Trust your neighbors: A comprehensive survey of neighborhood-based  methods for recommender systems

We investigate a novel cluster-of-bandit algorithm CAB for collaborative
recommendation tasks that implements the underlying feedback sharing mechanism
by estimating the neighborhood of users in a context-dependent manner. CAB
makes sharp departures from the state of the art by incorporating collaborative
effects into inference as well as learning processes in a manner that
seamlessly interleaving explore-exploit tradeoffs and collaborative steps. We
prove regret bounds under various assumptions on the data, which exhibit a
crisp dependence on the expected number of clusters over the users, a natural
measure of the statistical difficulty of the learning task. Experiments on
production and real-world datasets show that CAB offers significantly increased
prediction performance against a representative pool of state-of-the-art
methods.

CAB 算法是一种协同推荐算法，它采用了上下文相关的用户邻域估计机制，能够同时进行探索利用平衡和协同步骤，证明了具有不同数据假设的后悔边界，并在生产和真实数据集上验证了 CAB 的显着预测性能优势。

关于上下文相关赌博机聚类的研究

On Context-Dependent Clustering of Bandits

Collaborative recommendation is an information-filtering technique that
attempts to present information items that are likely of interest to an
Internet user. Traditionally, collaborative systems deal with situations with
two types of variables, users and items. In its most common form, the problem
is framed as trying to estimate ratings for items that have not yet been
consumed by a user. Despite wide-ranging literature, little is known about the
statistical properties of recommendation systems. In fact, no clear
probabilistic model even exists which would allow us to precisely describe the
mathematical forces driving collaborative filtering. To provide an initial
contribution to this, we propose to set out a general sequential stochastic
model for collaborative recommendation. We offer an in-depth analysis of the
so-called cosine-type nearest neighbor collaborative method, which is one of
the most widely used algorithms in collaborative filtering, and analyze its
asymptotic performance as the number of users grows. We establish consistency
of the procedure under mild assumptions on the model. Rates of convergence and
examples are also provided.

提出一种用于协作推荐的随机模型，深入分析了余弦型最近邻协作过滤算法的渐近性能，建立该程序在模型的温和假设下的一致性，并提供了收敛速度和例子。