[论文解读] Preference-based Graphic Models for Collaborative Filtering

Rong Jin, Luo Si+1 more|arXiv (Cornell University)|Oct 19, 2012

Recommender Systems and Techniques参考文献 12被引用 68

一句话总结

本文提出了两种新颖的贝叶斯图模型——解耦模型与偏好模型，通过显式区分用户偏好与评分行为，以改进协同过滤。解耦模型将偏好与评分分离，在两个电影评分数据集上显著优于五种现有方法，更好地捕捉了用户特定的评分偏差。

ABSTRACT

Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for collaborative filtering with promising results. However, while these models have succeeded in capturing the similarity among users and items in one way or the other, none of them has considered the fact that users with similar interests in items can have very different rating patterns; some users tend to assign a higher rating to all items than other users. In this paper, we propose and study of two new graphic models that address the distinction between user preferences and ratings. In one model, called the decoupled model, we introduce two different variables to decouple a users preferences FROM his ratings. IN the other, called the preference model, we model the orderings OF items preferred BY a USER, rather than the USERs numerical ratings of items. Empirical study over two datasets of movie ratings shows that appropriate modeling of the distinction between user preferences and ratings improves the performance substantially and consistently. Specifically, the proposed decoupled model outperforms all five existing approaches that we compare with significantly, but the preference model is not very successful. These results suggest that explicit modeling of the underlying user preferences is very important for collaborative filtering, but we can not afford ignoring the rating information completely.

研究动机与目标

• 解决现有协同过滤模型无法区分用户偏好与评分行为的局限性。
• 独立建模用户偏好，而非依赖数值评分，以提升预测准确率。
• 探究显式建模偏好排序是否能提升协同过滤性能。
• 评估将偏好与评分分离对推荐系统有效性的影响力。
• 通过解耦主观偏好与客观评分模式，提供对用户行为更细致的理解。

提出的方法

• 提出一种解耦模型，使用两个隐变量：一个用于用户偏好，另一个用于评分偏差。
• 使用对项目之间的偏好排序来建模用户偏好，而非数值评分。
• 使用贝叶斯图模型表示用户、项目及其隐偏好与评分变量之间的依赖关系。
• 应用概率推理来估计隐变量并预测缺失评分。
• 在两个真实世界的电影评分数据集上使用最大似然估计训练模型。
• 将所提模型的性能与五种成熟的协同过滤基线方法进行比较。

实验结果

研究问题

• RQ1 将用户偏好与评分行为分离是否能带来更好的协同过滤性能？
• RQ2 与数值评分相比，建模偏好排序如何影响推荐准确率？
• RQ3 在多大程度上考虑用户特定的评分偏差能提升预测质量？
• RQ4 与将偏好和评分混合作为单一信号的传统协同过滤方法相比，解耦模型是否更具有效性？
• RQ5 尽管理论上能捕捉偏好排序，为何偏好模型表现不佳？

主要发现

• 解耦模型在预测准确率方面显著优于所有五种对比基线方法。
• 显式建模用户特定评分偏差通过捕捉用户间评分尺度的系统性差异，提升了性能。
• 偏好模型（通过建模项目排序而非数值评分）未达到具有竞争力的性能。
• 实证结果表明，即使显式建模了偏好，完全忽略评分信息仍会导致性能下降。
• 研究证实，尽管偏好建模有益，但评分数据对于准确预测仍至关重要。
• 解耦模型的成功表明，评分偏差是传统协同过滤方法中常被忽视的关键因素。