Abstract
As a key criterion to measure ads performance, CVR quantitatively describes the proportion of users who take a desirable action (such as purchasing an item, adding to a cart, adding favorite items, etc.) on given ads in the ads ecosystem. Therefore, it is a critical issue to allocate ads-budget and increase advertisers profits. Focusing on improving the accuracy of CVR prediction in online advertising, this paper firstly analyzes and reveals the correlation underlying creatives associated with ads and CVR, which is excluded by most state-of-the-arts in this literature. Furthermore, we propose a novel LR+ model to utilize the potential impacts of creatives on predicting CVR. Experimental results and analysis on two public real-world datasets (REC-TMALL dataset and Taobao Clothes Matching dataset) validate the effectiveness of the proposed LR+ and demonstrate that the proposed LR+ outperforms typical models (e.g., LR, GBDT and linear SVR) in term of root mean square of error (RMSE).
Keywords
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This research was supported by the National Natural Science Foundation of China (7167010139).
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Jiang, J., Jiang, H. (2017). Conversion Rate Estimation in Online Advertising via Exploring Potential Impact of Creative. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_24
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DOI: https://doi.org/10.1007/978-3-319-64471-4_24
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