ArticleHeliyon2024
Computing advertising intelligent computing and push based on artificial intelligence in the big data era.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of big data, intelligent computation and precise targeting of advertisements rely on artificial intelligence (AI) technology to enhance advertising effectiveness and user experience. As advertising media shift from traditional outlets to the internet and mobile devices, manually filtering and targeting advertisements has become increasingly ineffective due to the vast amount of information and user groups. Consequently, AI-based advertisement computation and targeting technologies have emerged. This paper explores how modern technologies and AI applications can be used to achieve intelligent computation, filtering, and targeting of advertisements, thereby improving ad effectiveness and profitability. The study optimizes ad targeting effectiveness and increases return on investment (ROI) by comparing ad campaigns between Group A, which did not use ad targeting algorithms, and Group B, which employed AI-based targeting algorithms. The experimental results show that the average ROI for tourism, shopping, and rental ads in Group A were 153.03 %, 232.32 %, and 192.57 %, respectively, while Group B's average ROI were 173.96 %, 288.74 %, and 216.12 %. These results indicate that AI-based ad targeting algorithms can significantly improve the ROI of ad campaigns compared to non-algorithmic approaches, suggesting that ad targeting algorithms can help advertisers achieve higher profits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.