[论文解读] Empirical Analysis of Predictive Algorithms for Collaborative Filtering

John S. Breese, David Heckerman+1 more|arXiv (Cornell University)|Jan 30, 2013

Data Management and Algorithms参考文献 11被引用 4,510

一句话总结

本文比较了协同过滤的多种预测算法，包括基于相关性的、向量相似性和贝叶斯方法，在多个领域和评估指标上进行比较。

ABSTRACT

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.

研究动机与目标

• 评估不同协同过滤算法的预测准确性。
• 比较相关性、向量相似性和贝叶斯方法。
• 评估在多数据集、多协议和多评估指标下的性能。

提出的方法

• 实现并比较基于相关性的、向量相似性和贝叶斯方法变体的协同过滤。
• 使用两种评估指标：平均绝对偏差和排序列表的效用。
• 在三个应用领域、四个协议和两种指标上进行实验。

实验结果

研究问题

• RQ1 哪些预测算法（基于相关性、向量相似性、贝叶斯方法）在跨数据集的协同过滤预测任务中具有更高的准确性？
• RQ2 在不同评估指标与应用环境下，带决策树的贝叶斯网络如何与贝叶斯聚类和向量相似性方法进行比较？
• RQ3 哪些因素（数据集性质、排序与逐条呈现、投票可用性）会影响预测的首选方法？

主要发现

• 每个节点带决策树的贝叶斯网络以及相关性方法通常优于贝叶斯聚类和向量相似性方法。
• 首选方法取决于数据集特征和应用类型（排序呈现 vs 逐条呈现）。
• 性能取决于数据集大小、预测速度和学习时间。
• 结果在不同问题领域和实验协议中存在差异。