论文标题

针对原则的用户端推荐系统

Towards Principled User-side Recommender Systems

论文作者

Sato, Ryoma

论文摘要

传统上,推荐算法是为服务开发人员设计的。但是,最近,已经提出了一种称为用户端推荐系统的新范式,它们使Web服务用户能够构建自己的推荐系统,而无需访问交易秘密数据。即使该服务的官方推荐系统不公平,这种方法也为用户定义的公平系统打开了大门。虽然现有的用户端推荐系统的方法已经解决了构建建议系统的挑战性问题,但它们依赖于启发式方法,但从理论角度来看,尚不清楚构建用户端推荐系统是否是一个明确定义的问题。在本文中,我们提供了用户端推荐系统的理论理由。具体来说,我们看到可以从用户可用的信息中恢复隐藏的项目功能,从而使用户端推荐系统的构建定义明确。但是,这种理论上扎根的方法并非有效。为了实现实用但理论上合理的推荐系统,我们提出了用户端推荐系统的三个理想属性,并根据这些基金会提出了一个有效而有效的用户端推荐系统,\ textsc {consul}。我们证明\ textsc {consul}满足了所有三个属性,而现有的用户端推荐系统至少缺乏其中一个。在实验中,我们通过数值实验验证了特征恢复理论。我们还表明,我们所提出的方法在有效性和效率之间取得了良好的权衡,并通过案例研究证明了所提出的方法可以检索提供者的官方推荐系统无法进行的信息。

Traditionally, recommendation algorithms have been designed for service developers. However, recently, a new paradigm called user-side recommender systems has been proposed and they enable web service users to construct their own recommender systems without access to trade-secret data. This approach opens the door to user-defined fair systems even if the official recommender system of the service is not fair. While existing methods for user-side recommender systems have addressed the challenging problem of building recommender systems without using log data, they rely on heuristic approaches, and it is still unclear whether constructing user-side recommender systems is a well-defined problem from theoretical point of view. In this paper, we provide theoretical justification of user-side recommender systems. Specifically, we see that hidden item features can be recovered from the information available to the user, making the construction of user-side recommender system well-defined. However, this theoretically grounded approach is not efficient. To realize practical yet theoretically sound recommender systems, we propose three desirable properties of user-side recommender systems and propose an effective and efficient user-side recommender system, \textsc{Consul}, based on these foundations. We prove that \textsc{Consul} satisfies all three properties, whereas existing user-side recommender systems lack at least one of them. In the experiments, we empirically validate the theory of feature recovery via numerical experiments. We also show that our proposed method achieves an excellent trade-off between effectiveness and efficiency and demonstrate via case studies that the proposed method can retrieve information that the provider's official recommender system cannot.

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