[论文解读] Clustering-Based Matrix Factorization

Nima Mirbakhsh, Charles X. Ling|arXiv (Cornell University)|Jan 28, 2013

Recommender Systems and Techniques参考文献 13被引用 5

一句话总结

本文提出了一种基于聚类的矩阵分解方法，通过用户和项目聚类整合一般邻域信息，从而提升推荐准确性。通过将聚类的共享偏好融合到有偏置的矩阵分解模型中，该方法在Movielens100k和Netflix数据集上实现了优于或相当的性能，且所需邻居数量更少，相较于现有的邻域感知模型。

ABSTRACT

Recommender systems are emerging technologies that nowadays can be found in many applications such as Amazon, Netflix, and so on. These systems help users to find relevant information, recommendations, and their preferred items. Slightly improvement of the accuracy of these recommenders can highly affect the quality of recommendations. Matrix Factorization is a popular method in Recommendation Systems showing promising results in accuracy and complexity. In this paper we propose an extension of matrix factorization which adds general neighborhood information on the recommendation model. Users and items are clustered into different categories to see how these categories share preferences. We then employ these shared interests of categories in a fusion by Biased Matrix Factorization to achieve more accurate recommendations. This is a complement for the current neighborhood aware matrix factorization models which rely on using direct neighborhood information of users and items. The proposed model is tested on two well-known recommendation system datasets: Movielens100k and Netflix. Our experiment shows applying the general latent features of categories into factorized recommender models improves the accuracy of recommendations. The current neighborhood-aware models need a great number of neighbors to acheive good accuracies. To the best of our knowledge, the proposed model is better than or comparable with the current neighborhood-aware models when they consider fewer number of neighbors.

研究动机与目标

• 通过整合一般邻域信息，提升基于矩阵分解的推荐系统的推荐准确性。
• 通过聚类捕捉潜在偏好，减少邻域感知模型对大邻居集合的依赖。
• 通过引入聚类级别的潜在特征，增强有偏置矩阵分解的鲁棒性。
• 在真实世界数据集上评估所提出模型，并与最先进邻域感知方法进行性能比较。

提出的方法

• 将用户和项目聚类为类别，以识别群体之间的共享偏好模式。
• 提取代表聚类级别偏好的潜在特征，并将其融合到有偏置的矩阵分解框架中。
• 利用一般邻域信息，而非仅依赖直接的用户-项目邻域关系。
• 扩展有偏置矩阵分解以整合基于聚类的潜在特征，从而提高预测准确性。
• 使用标准推荐指标，在Movielens100k和Netflix数据集上进行模型训练和评估。

实验结果

研究问题

• RQ1 通过聚类整合一般邻域信息，能否提升矩阵分解模型的推荐准确性？
• RQ2 与现有邻域感知模型相比，该模型在较少邻居数量下的表现如何？
• RQ3 聚类级别的潜在特征在多大程度上提升了有偏置矩阵分解的预测性能？

主要发现

• 当使用较少邻居时，所提出的模型在推荐准确性上达到更高或相当的水平，优于或相当于最先进邻域感知模型。
• 整合聚类级别的共享偏好，提升了推荐模型的鲁棒性和泛化能力。
• 该方法减少了对大邻居集合的依赖，而这是当前邻域感知方法的一个局限。
• 在Movielens100k和Netflix上的实证评估证实，将聚类中的一般潜在特征整合到矩阵分解中具有有效性。