面向互联网大数据的用户兴趣挖掘及预测研究

项目名称： 面向互联网大数据的用户兴趣挖掘及预测研究

项目编号： No.61772445

项目类型： 面上项目

立项/批准年度： 2018

项目学科： 计算机科学学科

项目作者： 周志贤

作者单位： 香港城市大学深圳研究院

项目金额： 15万元

中文摘要： 互联网应用积累了海量的丰富数据，包括人的属性、物的属性、人与人的社交关系、人对物的互动关系。例如，用户浏览新闻、购买商品、签到旅游景点等，并且通过文本评论分享自己的经历。本项目拟利用这些数据，挖掘和预测用户的兴趣，这具有重要的商业、广告和人文价值。本项目将研究以下内容：1)建立基于社区、地区、时间和互动类型的主题情感模型，以挖掘用户对事物的细粒度兴趣。2)通过融合矩阵分解和基于核函数的多属性非线性回归，提出新的混合评分模型，以预测用户对事物的总体评分。3)建立主题情感挖掘模型和混合评分预测模型的联系，即通过主题情感模型估计历史用户-事物评分矩阵，作为混合评分预测模型的输入，以解决互联网应用中历史评分矩阵缺乏问题，从而提高混合评分预测模型的可用性。4)利用互联网大数据和用户实验数据，评价本项目提出的模型的效果。本项目开发的用户兴趣挖掘及预测技术，可应用于推荐系统，为大众和商家带来便利和利益。

中文关键词： 用户兴趣挖掘；用户兴趣预测；主题情感挖掘；评分预测；推荐系统

英文摘要： Nowadays, the applications in the Internet have accumulated huge and rich data, including the attributes of users and items, the social links between users, and the interactions between users and items. For example, users would like to read news, buy products, or check in tourism attractions, and then write comments to share their experiences. This project aims to exploit these data to mine and predict the interests of users to items, which can bring benefits to business, advertising, and the public. This project will study the following contents. 1) We will propose a new topic-sentiment model based on the dependencies on communities, regions, time, and the types of interactions in order to mine the fine-grained interests of users on items. 2) We will develop a new hybrid rating prediction model by combining matrix factorization and kernel-based nonlinear regression in order to predict the overall rating of users on items. 3) We will build the correlation between the topic-sentiment model and the hybrid rating prediction model. That is, the topic-sentiment model is used to estimate the historical user-item rating matrix from textual comments which is input into the hybrid rating prediction model. Thus, we can solve the problem on the lack of historical user-item rating matrices in the applications of the Internet and make the hybrid rating prediction model more useable. 4) We will evaluate the effectiveness of the proposed models using the real data from the Internet and user studies. The techniques on mining and predicting user interests can be applied in recommender systems so as to boost business profits and enrich people's quality of life.

英文关键词： user interest mining;user interest predicting;aspect-based opinion mining;rating prediction;recommender systems