A contextual approach to improve the user's experience in interactive recommendation systems

N. Silva, Heitor Werneck, Thiago Silva, A. Pereira, Leonardo Rocha
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引用次数: 4

Abstract

Recommendation Systems have concerned about the online environment of real-world scenarios where the system should continually learn and predict new recommendations. Current works have handled it as a Multi-Armed Bandit (MAB) problem by proposing parametric bandit models based on the main recommendation concepts to handle the exploitation and exploration dilemma. However, recent works identified a new problem about the way these models handle the user cold-start. Due to the lack of information about the user, these models have intrinsically delivered naive non-personalized recommendations in their first recommendations until the system learns more about the user. The first recommendations of these bandit models are equivalent to a random selection around the items (i.e., a pure-exploration approach) or a biased selection by the most popular items in the system (i.e., a pure-exploitation approach). Thus, to mitigate this problem, we propose a new contextual approach to initialize the bandit models. This context is made by the information available about the items: their popularity and entropy. The idea is to address both exploration and exploitation goals since the first recommendations by mixing entropic and popular items. Indeed, this approach maximizes the user's satisfaction in the long-term run. By a strong experimental evaluation, comparing our proposal with seven state-of-the-art methods in three real datasets, we demonstrate this context achieves statistically significant improvements by outperforming all baselines.
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一种改善交互式推荐系统中用户体验的上下文方法
推荐系统关注现实世界场景的在线环境,系统应该不断学习和预测新的建议。目前的研究将其作为一个多武装盗匪(Multi-Armed Bandit, MAB)问题来处理,提出了基于主要推荐概念的参数化盗匪模型来处理开发和勘探困境。然而,最近的研究发现了一个关于这些模型处理用户冷启动方式的新问题。由于缺乏关于用户的信息,这些模型本质上在它们的第一次推荐中提供了幼稚的非个性化推荐,直到系统了解更多关于用户的信息。这些强盗模型的第一个建议相当于围绕物品的随机选择(即,纯探索方法)或系统中最受欢迎的物品的有偏见选择(即,纯开发方法)。因此,为了缓解这个问题,我们提出了一种新的上下文方法来初始化强盗模型。这个上下文是由关于项目的可用信息构成的:它们的受欢迎程度和熵。这个想法是通过混合熵项和流行项来解决探索和开发目标。事实上,从长远来看,这种方法最大限度地提高了用户的满意度。通过强有力的实验评估,将我们的建议与三个真实数据集中的七种最先进的方法进行比较,我们证明了这一背景通过优于所有基线实现了统计上显着的改进。
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