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Boosting Response Aware Model-Based Collaborative Filtering

机译:促进基于响应感知模型的协同过滤

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摘要

Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users’ preference based on users’ previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: 1) most CF methods ignore users’ response patterns and may yield and suboptimal performance; 2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and 3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users’ response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation.
机译:推荐系统有望提供个性化的收藏服务。协作过滤(CF)技术基于用户以前的行为来预测用户的偏好,已成为构建现代推荐系统的最成功技术之一。先前提出的CF方法中出现了几个具有挑战性的问题:1)大多数CF方法忽略了用户的响应模式,可能会导致性能下降和性能欠佳; 2)一些CF方法采用启发式权重设置,缺乏系统的实现; 3)多项式混合模型可能会削弱矩阵分解生成数据矩阵的计算能力,从而增加训练的计算成本。为了解决这些问题,我们将用户的响应模型合并到概率矩阵分解CF模型(概率矩阵分解CF)中,以建立响应感知概率矩阵分解(RAPMF)框架。更具体地,我们根据伯努利分布对用户响应进行假设,该伯努利分布由观察到的等级的等级分数参数化,而未观察到的等级的阶跃函数则由参数决定。此外,我们通过小批量实现和精心设计的调度策略来加快算法的速度。最后,我们设计了不同的实验方案,并对合成数据集和真实数据集进行了系统的经验评估,以证明所提出的RAPMF及其微型批量实施的优点。

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