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Clustering based online learning in recommender systems: A bandit approach

机译：推荐系统中基于聚类的在线学习：一种强盗方法

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A big challenge for the design and implementation of large-scale online services is determining what items to recommend to their users. For instance, Netflix makes movie recommendations; Amazon makes product recommendations; and Yahoo! makes webpage recommendations. In these systems, items are recommended based on the characteristics and circumstances of the users, which are provided to the recommender as contexts (e.g., search history, time, and location). The task of building an efficient recommender system is challenging due to the fact that both the item space and the context space are very large. Existing works either focus on a large item space without contexts, large context space with small number of items, or they jointly consider the space of items and contexts together to solve the online recommendation problem. In contrast, we develop an algorithm that does exploration and exploitation in the context space and the item space separately, and develop an algorithm that combines clustering of the items with information aggregation in the context space. Basically, given a user's context, our algorithm aggregates its past history over a ball centered on the user's context, whose radius decreases at a rate that allows sufficiently accurate estimates of the payoffs such that the recommended payoffs converge to the true (unknown) payoffs. Theoretical results show that our algorithm can achieve a sublinear learning regret in time, namely the payoff difference of the oracle optimal benchmark, where the preferences of users on certain items in certain context are known, and our algorithm, where the information is incomplete. Numerical results show that our algorithm significantly outperforms (over 48%) the existing algorithms in terms of regret.
机译：大型在线服务的设计和实现面临的一大挑战是确定向其用户推荐哪些项目。例如，Netflix提供电影推荐;亚马逊提出产品推荐;和雅虎！提出网页建议。在这些系统中，根据用户的特征和环境来推荐项目，将其作为上下文（例如，搜索历史，时间和位置）提供给推荐者。由于项目空间和上下文空间都很大，因此，构建高效的推荐系统的任务是具有挑战性的。现有作品要么集中在没有上下文的大项目空间上，要么集中在具有少量项目的大上下文空间上，或者他们共同考虑项目和上下文的空间以解决在线推荐问题。相反，我们开发了一种在上下文空间和项目空间中分别进行探索和开发的算法，并开发了一种将项目的聚类与上下文空间中的信息聚合相结合的算法。基本上，在给定用户上下文的情况下，我们的算法将其过去的历史汇总到以用户上下文为中心的球上，该球的半径以允许足够准确地估计收益的速率减小，从而使推荐的收益收敛到真实的（未知的）收益。理论结果表明，该算法可以及时获得亚线性学习后悔，即已知最优条件下用户对某些项目的偏好已知的oracle最优基准的收益差异，以及信息不完整的算法。数值结果表明，就遗憾而言，我们的算法明显优于现有算法（超过48％）。

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