基于市场效应的用户行为与协同过滤推荐研究

项目名称： 基于市场效应的用户行为与协同过滤推荐研究

项目编号： No.71462036

项目类型： 地区科学基金项目

立项/批准年度： 2015

项目学科： 管理科学

项目作者： 赵昆

作者单位： 云南财经大学

项目金额： 35万元

中文摘要： 推荐系统是当前包括电子商务在内的各种网络应用的一个重要组成部分。课题在深入分析当前协同过滤推荐技术存在的问题的基础上，结合信息技术应用发展、尤其是大数据背景下的应用需求，针对人们在产品选择中存在的一些行为特征，提出效应行为概念，拟将在对其进行深入的理论和实证研究基础上，展开相应的推荐算法研究。研究内容包括基于市场效应的用户行为研究、推荐算法及其应用相关问题研究、相关推荐技术比较分析等三个方面。 课题对研究方案进行了充分论证和精心设计，研究思路新颖独特。对效应行为的研究，可以拓展对人们选择行为的认识框架，以此为基础进行推荐算法的分析设计，对有效解决推荐系统应用中数据的稀疏性、推荐的精确性与系统效率之间的矛盾等方面的问题有较高的理论和实际应用价值，使推荐系统在大数据背景下具有较强的适应性。研究成果将丰富推荐系统领域现有的理论和方法，对完善我国网络购物环境、促进电子商务应用发展具有积极意义。

中文关键词： 电子商务；用户行为；推荐系统；协同过滤；大数据

英文摘要： Recommender system is one of the most important parts in a variety of Internet-based applications, including e-commerce and e-business. Based on the deep analysis into the problems existed in current collaborative filtering recommendation methods used in the systems, combined with the understanding of the requirements of information technology development, especially the requirements from big data applications, the project will carry out research on a sort of phenomenon appeared in the activities of users' product selection. The phenomenon is referred to as effect-based behavior in the project and will be examined from both theoretical and empirical perspective. The understanding of the phenomenon is beloved to be able to enlighten approaches for research on recommendation methods and this project will also conduct research on new recommendation methods from this view. The project will address research topics in three aspects including study of effect-based user behavior, research on recommendation methods and related application issues, experiments on and comparisons among the related recommendation methods. Base on the original concept of effect-based behavior proposed in this project, a distinctive approach is suggested to deal with the associated questions in recommender systems research area. Full consideration has been given to the related aspects of the proposed project and the scheme of the research is well demonstrated in the proposal. The proposed concept is believed to be helpful in extending our understanding about user behavior in product selection and evaluation. Recommendation methods generated from this new understanding about user behavior are believed to be of effectiveness in solving problems such as data sparsity, the trade-off between the efficiency and accuracy of a recommender system, and etc., which exist in traditional recommendation methods, of both value in theoretical research and applications in real practices, and of help in enhancing the adaptability of recommender systems in big data application environment. After all, the research is hoped to gain fruitful results which will enrich theories and methods in recommender systems area, and has its significance in improving shopping environment for online users in their related network activities, has implication in booming the development of e-commerce application.

英文关键词： e-commerce;user behavior;recommender systems;collaborative filtering;big data