b2c环境下用户感知可信的推荐系统理论及其关键方法分析word格式论文.docx

AbstractWith the development of E-commerce and shopping platform, online shoping is gradually accepted by people. Because of the goods are in great abundance, in order to improve service quality the website needs a tool to provide goods for consumers quickly and precisely. Recommendation system is not only a way to improve consumer’s decision quality and decision efficiency, but also help website increase profits and decrease costs. Therefore, recommender system has enormous value to research and apply.In research area there are enormous research achievements about recommender system. These findings not only improve the precise of recommendation but also meet consumer’s novel needs. However, recommender system should obtain the trust from consumers as a precondition for realizing it’s role. If there is no trust, it will make the effects of recommender on consumers and website big sell at a discount. So, the purpose of this paper is to design a trust-based recommender system. Through interacting with system, the consumer will perceive benevolence, integrity and competence of system. The research process is based on the information system design theory. The research process and contents follows:We first constructed trust-based theory model through theoretical derivation. The model could explain consumer’s process of trust formation gradually through interacting with recommender system. The designed system reflects a supporting for consumer’s purchase decision process. Consumer’s trust develops when one gains knowledge about trustee on the process of interacting. In detail, we analized the effects of consumer’s task-system function fit on willing to use initially based on task-technology fit model. Howerver according to Elaboration Likelihood Model, active engagement is important for consumers to understand and evaluate system, so consumers’s good experience is very important. Therefor we analized the effects of characters of system, feedback, skill-challenge fit,