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The Impact of Demographic Factors on Persuasion Strategies in Personalized Recommender System

机译:个性化推荐系统中人口因素对说服策略的影响

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A recommender system is an information filtering tool that copes with the growing volume of information and helps the user to make faster decisions by providing products and services matched with their needs and interests. However, a large number of users are not satisfied with the provided recommendations and do not accept them. Based on the Elaboration Likelihood Model (ELM), If supplementary information about recommendations is provided, those users having the low motivation and capability to analyze the usefulness of the recommended item can be persuaded to accept it. This paper focuses on analyzing the impact of demographic factors on increasing the acceptance of recommendations. This study was conducted by a web-based online survey. The movie's recommender system has been developed along with the explanations based on Cialdini's persuasion strategies as the peripheral cues. The collected data are analyzed through statistical techniques using the SPSS software. The results show that the persuasiveness degree of the persuasion strategies differs related to individuals with the different demographic factors.
机译:推荐系统是一种信息过滤工具,可应对不断增长的信息量,并通过提供与其需求和兴趣相匹配的产品和服务来帮助用户做出更快的决策。但是,许多用户对所提供的建议不满意并且不接受它们。基于细化可能性模型(ELM),如果提供了有关建议的补充信息,则可以说服那些动机和能力较低的用户来分析推荐项目的实用性。本文重点分析人口统计学因素对增加推荐接受度的影响。这项研究是通过基于网络的在线调查进行的。这部电影的推荐器系统是根据Cialdini的说服策略作为外围提示而开发的,并进行了解释。使用SPSS软件通过统计技术分析收集的数据。结果表明,说服策略的说服程度因人口因素不同而不同。

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