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Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering

机译:成对概率矩阵分解用于隐式反馈协同过滤

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Collaborative filtering (CF) has been widely applied to improve the performance of recommendation systems. With the motivation of the Netflix Prize, researchers have proposed a series of CF algorithms for rating datasets, such as the 1 to 5 rating on Netflix. In this paper, we investigate the problem about implicit user feedback, which is a more common scenario (e.g. purchase history, click-through log, and page visitation). In these problems, the training data are only binary, reflecting the user's action or inaction. Under these circumstances, generating a personalized ranking list for every user is a more challenging task since we have less prior information. We consider it as a ranking problem: collaborative ranking (CR) skips the intermediate rating prediction step, and creates the ranked list directly. In order to solve the ranking problem, we propose a new model named pairwise probabilistic matrix factorization (PPMF), which takes a pairwise ranking approach integrated with the popular probabilistic matrix factorization (PMF) model to learn the relative preference for items. Experiments on benchmark datasets show that our proposed PPMF model outperforms the state-of-the-art implicit feedback collaborative ranking models by using different evaluation metrics.
机译:协作过滤(CF)已被广泛应用于改善推荐系统的性能。在Netflix奖的激励下,研究人员提出了一系列针对分级数据集的CF算法,例如Netflix上的1到5分级。在本文中,我们调查了有关隐式用户反馈的问题,后者是一种较为常见的情况(例如购买历史记录,点击日志和页面访问)。在这些问题中,训练数据只是二进制的,反映了用户的作为或不作为。在这种情况下,为每个用户生成个性化的排名列表是一项更具挑战性的任务,因为我们的先验信息较少。我们将其视为排名问题:协作排名(CR)跳过了中间评分预测步骤,并直接创建了排名列表。为了解决排名问题,我们提出了一种新的模型,称为成对概率矩阵分解(PPMF),该模型采用成对排名方法与流行的概率矩阵分解(PMF)模型集成,以了解项目的相对偏好。在基准数据集上进行的实验表明,通过使用不同的评估指标,我们提出的PPMF模型优于最新的隐式反馈协作排名模型。

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