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The impact of information factors on online recommendation adoption

机译:信息因素对在线推荐采用的影响

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With the advent of large data age, the recommendation system began to enter people's lives, according to the user's habits, how to recommend will become the future business development trend. In this paper, we used Elaboration Likelihood Model (ELM) to demonstrate the important adoption factors on the recommendation system such as the recommendation persuasiveness, recommendation source credibility, recommendation completeness and recommendation credibility. Finally, we show that only recommendation information completeness is not significant on readers' perception of recommendation credibility, others are best for it. And recommendation source credibility and recommendation credibility are both effective effect on recommendation adoption.
机译:随着大数据时代的到来,推荐系统开始进入人们的生活,根据用户的习惯,如何推荐将成为未来业务发展的趋势。在本文中,我们使用细化可能性模型(ELM)来说明推荐系统中重要的采用因素,例如推荐的说服力,推荐来源的可信度,推荐的完整性和推荐的可信度。最后,我们表明,只有推荐信息的完整性对读者对推荐可信度的感知并不重要,而其他人则最能做到这一点。推荐来源可信度和推荐可信度均对推荐采用产生有效影响。

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