基于在线问答社区的智能信息服务方法及其用户决策影响研究

当前，在线问答社区已成为一种新兴的互联网信息服务平台，吸引了大量的用户和企业的关注。与传统搜索引擎不同，在线问答社区以问答形式提供信息服务，所提供信息具有高度相关性和专业性的特点。因此，平台上的问答讨论经常被转载到各种社交媒体，对消费者和企业的影响也越来越大。但现有平台依然存在诸如信息冗余、信息过载、无回复问题等影响平台信息服务质量的关键问题。针对这些问题，本项目从用户在线问答社区中获取信息的全过程入手，着重研究一种智能信息服务方法，该方法包含三个逐步深入的环节：（1）冗余问答识别及抽取；（2）专家用户推荐；（3）用户比较意见抽取。同时，基于所提出的智能信息服务方法，本项目进一步研究探讨了这种方法对用户决策的影响机制。通过本项目的研究成果，能够帮助在线问答社区更好的提升信息服务质量，帮助消费者及时高效的获取所需决策信息，同时也有助于企业掌握消费者舆情并把握未来市场的走向。

Community Question Answering (CQA) has become an emerging information service platform on the Internet, attracting a large number of users and business concerns. Unlike traditional search engines, CQA provides information services in the form of questions and answers, which are highly relevant and professional. Therefore, discussions in CQA are often reproduced in a variety of social media, leading to the growing impact on consumers and businesses. However, existing CQA platforms still suffer from key issues, such as information redundancy, information overload, no recovery problems, etc., which highly affect the information service quality of CQA. In view of those problems, this project starts from the whole process of user information acquisition in CQA, and focuses on the research of an intelligent information service method, which includes three layer-by-layer steps: (1) redundant question and answer identification and extraction; (2) expert user recommendation; (3) user comparison opinion extraction. Meanwhile, this project further explores the mechanism that the results of the proposed information service method impact on user decision. Through the research results of this project, it can help CQA platforms improve the quality of information service, serve consumers by providing timely and efficient decision-making information, and also help enterprises grasp the public opinion and predict the future market trend.