Detection of the customer time-variant pattern for improving recommender systems
Detection of the customer time-variant pattern for improving recommender systems
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DOI：
10.1016/j.eswa.2004.10.001
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发表时间：
2005-02-01
影响因子：
8.5
通讯作者：
Han, I
中科院分区：
文献类型：
--
作者：
Min, SH;Han, I
由于电子商务的爆炸性增长，推荐系统正迅速成为加速交叉销售和增强客户忠诚度的核心工具。构建推荐系统有两种流行的方法--基于内容的推荐和协作过滤。到目前为止，协同过滤推荐系统在信息过滤和电子商务领域都取得了很大的成功。然而，目前的推荐研究很少关注时间相关数据在推荐过程中的使用。为了提高协同过滤推荐的性能，提出了一种检测用户时变模式的方法。该方法包括分析、检测更改和建议三个阶段。所提出的方法使用不同时间段的客户数据来检测客户行为的变化，并使用有关变化的信息来提高推荐的性能。(C)2004爱思唯尔有限公司。保留所有权利。
Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems-content-based recommending and collaborative filtering. So far, collaborative filtering recommender systems have been very successful in both information filtering and e-commerce domains. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest.This paper suggests a methodology for detecting a user's time-variant pattern in order to improve the performance of collaborative filtering recommendations. The methodology consists of three phases of profiling, detecting changes, and recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. (C) 2004 Elsevier Ltd. All rights reserved.