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Modeling Contextual Agreement in Preferences

机译：在首选项中建模上下文协议

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摘要

Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We therefore postulate the need to pay attention to "context", which refers to the specific-item on which the preferences between two users are to be estimated. In this paper, we propose a generative model for contextual agreement in preferences. For every triplet consisting of two users and an item, the model estimates both the prior probability of agreement between the two users, as well as the posterior probability of agreement with respect to the item at hand. The model parameters are estimated from ratings data. To extend the model to unseen ratings, we further propose several matrix factorization techniques focused on predicting agreement, rather than ratings. Experiments on real-life data show that our model yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.
机译：个性化或自定义每个用户的体验，被视为浏览当今Web上众多选择的一种有用方法。个性化的主要原则是能够对用户的偏好进行建模。范式已经从个人偏好的模式转变为共享偏好的模式，在个人偏好模式下，我们仅查看用户的过去活动，在共享偏好模式下，我们对用户对（例如，朋友，兴趣相似的人）之间偏好的相似性进行建模。但是，共享的首选项仍然过于精细，因为它假定一对用户将在所有项目之间共享首选项。因此，我们假设需要注意“上下文”，这是指要估计两个用户之间的偏好的特定项目。在本文中，我们提出了偏好中的情境协议的生成模型。对于由两个用户和一个项目组成的每个三元组，该模型既估算两个用户之间达成协议的先验概率，也估算与当前项目有关的后验概率。模型参数是根据额定数据估算的。为了将模型扩展到看不见的评级，我们进一步提出了几种专注于预测一致性而不是评级的矩阵分解技术。对现实生活数据的实验表明，与依赖共享首选项的模型相比，我们的模型产生的特定于上下文的相似性值在预测任务上表现更好。

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