Product Recommendation System Based on Personal Preference Model Using CAM
Product Recommendation System Based on Personal Preference Model Using CAM
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基于CAM的个人偏好模型的产品推荐系统
DOI：
10.1527/tjsai.20.346
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Long Zhang
中科院分区：
文献类型：
--
作者：
Ying Chen;Wei Lu;Xiaoyan Chen;L. Tang;Fangyan Rao;Qingbo Wang;Long Zhang
产品推荐系统是利用数据维护技术获得的业务规则实现的。购买的人口统计模式等业务规则能够覆盖有购买倾向的用户群体，但仅仅利用这些规则很难推荐适合各种个人偏好的产品。此外，收集大量高质量的调查数据是非常昂贵的，这是基于个人偏好模型的良好推荐所必需的。需要一种不需要问卷调查就能自动收集感性信息的方法。由于用户输入偏好数据的成本较高，因此从较少的偏好数据构建个人偏好模型也是必要的。本文提出了基于文本挖掘提取的感性信息和基于类别导向自适应建模（Category-guided Adaptive Modeling，简称CAM）构建的用户偏好模型的产品推荐系统。CAM是一种特征构建方法，它可以在相同标记样例距离较近、不同标记样例距离较远的空间中生成新的特征。在喜欢和不喜欢类别信息较少的情况下，CAM可以构建个人偏好模型。在系统中，检索代理收集产品规格，用户代理管理偏好模型和用户的好恶。将文本挖掘技术应用于网站上的产品信誉文档，获得产品的感性信息。为了验证所建立的偏好模型的有效性，我们进行了一些实验研究。
Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.