The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance.

本文介绍了一个基于随机梯度方法的联邦协作过滤模型，并将其应用于用户推荐系统，旨在提高用户隐私保护，且实验表明，在联邦实现中，相较于标准实现而言，推荐系统的性能并未降低。

面向隐私保护的联邦协作过滤个性化推荐系统

Federated Collaborative Filtering for Privacy-Preserving Personalized  Recommendation System

Many tasks in music information retrieval, such as recommendation, and
playlist generation for online radio, fall naturally into the query-by-example
setting, wherein a user queries the system by providing a song, and the system
responds with a list of relevant or similar song recommendations. Such
applications ultimately depend on the notion of similarity between items to
produce high-quality results. Current state-of-the-art systems employ
collaborative filter methods to represent musical items, effectively comparing
items in terms of their constituent users. While collaborative filter
techniques perform well when historical data is available for each item, their
reliance on historical data impedes performance on novel or unpopular items. To
combat this problem, practitioners rely on content-based similarity, which
naturally extends to novel items, but is typically out-performed by
collaborative filter methods.
In this article, we propose a method for optimizing contentbased similarity
by learning from a sample of collaborative filter data. The optimized
content-based similarity metric can then be applied to answer queries on novel
and unpopular items, while still maintaining high recommendation accuracy. The
proposed system yields accurate and efficient representations of audio content,
and experimental results show significant improvements in accuracy over
competing content-based recommendation techniques.

本文提出一种由协同过滤数据样本优化得到的基于内容相似度度量的音乐推荐系统，能够对于新颖或不受欢迎音乐领域的查询给出高精度和高效率的回答，并且在实验中得出了显著的精度改善。

学习内容相似度进行音乐推荐

Learning content similarity for music recommendation

In this paper, we analyze a collaborative filter that answers the simple
question: What is popular amongst your friends? While this basic principle
seems to be prevalent in many practical implementations, there does not appear
to be much theoretical analysis of its performance. In this paper, we partly
fill this gap. While recent works on this topic, such as the low-rank matrix
completion literature, consider the probability of error in recovering the
entire rating matrix, we consider probability of an error in an individual
recommendation (bit error rate (BER)). For a mathematical model introduced in
[1],[2], we identify three regimes of operation for our algorithm (named
Popularity Amongst Friends (PAF)) in the limit as the matrix size grows to
infinity. In a regime characterized by large number of samples and small
degrees of freedom (defined precisely for the model in the paper), the
asymptotic BER is zero; in a regime characterized by large number of samples
and large degrees of freedom, the asymptotic BER is bounded away from 0 and 1/2
(and is identified exactly except for a special case); and in a regime
characterized by a small number of samples, the algorithm fails. We also
present numerical results for the MovieLens and Netflix datasets. We discuss
the empirical performance in light of our theoretical results and compare with
an approach based on low-rank matrix completion.

本文分析了一种基于协作过滤的推荐算法的性能，考虑了单次推荐的误差概率，并通过数学模型的分析，确定了算法的三个操作区间以及性能表现，同时提供了 MovieLens 和 Netflix 数据集的实证结果。