面向消费行为大数据的用户建模与个性化推荐研究

项目名称： 面向消费行为大数据的用户建模与个性化推荐研究

项目编号： No.61472401

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 郭嘉丰

作者单位： 中国科学院计算技术研究所

项目金额： 84万元

中文摘要： 随着以电子商务、O2O为代表的互联网消费服务的高速发展，用户个性化、多样化的消费需求与庞大、信息无结构的消费品数据之间存在的信息鸿沟也愈发明显，如何深入分析用户的消费行为，识别用户的偏好兴趣，进而提供个性化推荐来刺激用户的潜在消费需求，是弥合信息鸿沟、提高社会消费能力的关键难题。用户消费行为大数据为解决该问题提供了契机，然而用户消费行为复杂关联、用户偏好异构多态、用户数据实时交互的特性，都给传统的用户建模与推荐技术提出了全新的挑战。针对上述挑战，本课题围绕复杂关联数据下的用户兴趣表征、面向偏好异构的个性化推荐问题建模、面向交互的在线推荐学习理论和评估机制三个科学问题，从用户兴趣画像、协同排序推荐模型、在线学习推荐技术和在线验证四个层面展开研究，旨在建立面向消费行为大数据的用户建模与个性化推荐成体系的基础理论和关键技术，推动用户建模与推荐在互联网消费服务行业的应用，进一步促进我国的社会消费。

中文关键词： 消费行为大数据；用户建模；个性化推荐

英文摘要： With the rapid increase of Web based consumption service like e-commerce and O2O, the information gap between diverse personalized user needs and large scale unstructured comsumption goods has become larger and larger. It has become an critical task to solve the gap and increase consumption capability of the society by analyzing users' consumption behaviors, identifying users' preferences and interests, and further providing personalized recommendation to help stimulate users' potention consumption needs. The big consumption data provide the opportunity to solve this problem. However, the complicated relations among user consumption behaviors, the heterogeneity and polymorphism of user's preferences, and the dynamic and interactive characteristics of user data all bring new challenges to the conventional user modeling and recommender systems. To address these chanllenges, we propose to focus on the following three scientific problems, i.e. user interest representation based on complicated linked data, personalized recommendation modeling based on heterogeneous preferences, and online learning theory and evaluation mechanism for recommendation based on interactions. We study from the following four aspects, namely user interest profiling, collaborative ranking based recommendation, online recommendation techniques and online demonstration. Through our study, we aim to build the systematic theory foundation and critical techniques for user modeling and personalized recommendation based on big consumption data. In this way, we can make better application of user modeling and recommender systems in Web based consumption services, and furhter improve the consumption capability of the society.

英文关键词： Big Consumption Data;User Modeling;Personalized Recommendation