The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.

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基于关联规则推荐的可扩展性

基于关联规则的方法是构建推荐系统最常用的技术之一，它已被广泛用于商业用途。已经开发了多种技术，主要包括合格规则选择和多规则组合，以创建有效的推荐。不幸的是，很少有人关注基于规则的推荐方法的可扩展性问题。然而，基于规则的方法的计算复杂度将随着在线客户和规则的增长而急剧增加，在典型的电子商务平台中通常为数百万。此外，用户行为的动态变化需要基于规则的方法近乎实时地进行推荐，这进一步凸显了基于规则的推荐系统的可扩展性问题。在本文中，我们提出了一个分布式框架，可以统一扩展不同的基于关联规则的推荐方法。具体来说，在总结现有的基于规则的方法的基础上，定义了一个通用的树型结构来存储不同种类的模式，并设计了一种高效的算法来挖掘符合条件的模式并计算推荐分数。为了应对不断增长的在线客户数量，提出了一种分布式框架，其中提出了两种负载均衡的分区树策略，分别适应稀疏和密集数据。对五个真实数据集的大量实验表明，所提出的框架可以显着提高基于关联规则的推荐系统的效率。