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Received:December 07, 2010 Revised:January 21, 2011
Received:December 07, 2010 Revised:January 21, 2011
中文摘要: 协同过滤推荐算法是电子商务个性化推荐系统中采用最为广泛的推荐技术,但是传统的推荐方法在进行商品推荐时忽略了交易时间和产品的价格因素,从而导致推荐质量下降.针对这一问题,提出了考虑时间和价格因素的协同过滤模型,通过实验表明在计算Pearson相关系数时考虑时间和价格因素对算法的改进最为有效.
Abstract:The collaborative filtering recommendation algorithm is the widely used technology in the personalized e-commerce recommendation system. However, the traditional recommendation algorithm neglected trading hours and product pricing when recommended products, which led to the lower quality recommended. To solve this problem, a collaborative filtering model considering time and price factors is proposed in this paper, and experiments show that the improvement of algorithm is most effective when time and price factors are taken into account in the calculation of Pearson correlation coefficient.
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基金项目:辽宁省教育厅项目(2009A326);中国煤炭工业协会项目(MTKJ2010-320),;教育部人文社科项目(10YJC630407)
引用文本：
赵宏霞,杨皎平,万君.考虑时间和价格因素的Web客户需求协同推荐模型.计算机系统应用,2011,20(8):91-94
ZHAO Hong-Xia,YANG Jiao-Ping,WAN Jun.Collaborative Filtering Recommendation Model of Web Customer Demand Considering Time and Price Factors.COMPUTER SYSTEMS APPLICATIONS,2011,20(8):91-94
赵宏霞,杨皎平,万君.考虑时间和价格因素的Web客户需求协同推荐模型.计算机系统应用,2011,20(8):91-94
ZHAO Hong-Xia,YANG Jiao-Ping,WAN Jun.Collaborative Filtering Recommendation Model of Web Customer Demand Considering Time and Price Factors.COMPUTER SYSTEMS APPLICATIONS,2011,20(8):91-94
