How Do Users View Personalized Ads?

In mobile Internet era, reading the information flow has become the mainstream way to consumption their fragmentation time. By using user portraits and their behavior log through artificial intelligence and big data technology, the mass of information content is delivered to each user.Benefit form the rapid growth of mobile Internet advertising, streaming Ads with its high exposure traffic and user label system has become advertisers competing home. Tencent company has achieved 7.499 billion RMB of advertising revenue through streaming Ads. And, Byte Beat is expected to generate revenue of 6 billion RMB in revenue this year.Because of its strong liquidity, Internet advertising platform and advertisers are full in love with streaming Ads. However, the research of streaming Ads is mainly in technical implementation level, few of paper study about how user view streaming Ads, and how its affect the user's advertising behavior?First of all, this paper reviews the literature on the personalized advertising, and analyzes the research status of personalized advertising.And then, we introduce three variables, namely, user perceived personalization, perceived relevance and user satisfaction with APP, and constructed a moderated mediation model. Second, we analyzed the data of questionnaire survey by using SPSS23 and AMOS20. The analysis included reliability analysis and validity analysis, and the most important,we analyzed the mediating effect and the moderating effect of this paper,and draws the following main conclusions:(1)Perceived relevance has a positive effect on the effectiveness of streaming Ads;(2)Perceived relevance plays a mediating role in the user's perception of streaming Ads and the attitude of the advertising brand/willingness to click on the ads.(3)The effect of streaming Ads is affected by the adjustment factors.The user's satisfaction with APP is regulated by the relationship between perceived personalization and advertising brand attitude. (4)The moderated mediation model is based with perceived personalization?perceived relevance and the attitude of the advertising brand/willingness to click on the ads.At last, based on hypothesis testing and model validation results,respectively, making recommendations for platform and advertisers. For the platform, they should increase the relevance of ads which is based on the user's basic information and operation log. At the same time, the platform should focus on the user experience and enhance product reputation, to seek a win-win situation between the user experience and business purposes. For the advertisers, they should rich ad styles and create native ads for our users.