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Feature Selection and Accuracy Enhancement Methods for Intelligent Advertising Bidding and Ranking Models
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Abstract
In large-scale advertising delivery scenarios, intelligent bidding and ranking models directly determine exposure efficiency and revenue performance. Model accuracy is often constrained by redundant features, noisy interference, and cumulative bias, due to the high dimensionality of features, dynamic distribution shifts, and delayed feedback. To address these challenges, a multi-source hierarchical feature system with stability screening can enhance feature effectiveness and interpretability. Combined with multi-task learning, bias correction, and re-ranking constraints, the linkage optimization between bidding and ranking is further strengthened. Moreover, closed-loop validation through offline evaluation and online experimentation ensures stability and scalability under real traffic. Empirical results indicate that this methodological framework reduces computational costs while simultaneously improving prediction accuracy and delivery returns, offering a feasible pathway for constructing efficient and robust bidding and ranking systems on advertising platforms.
Keywords
Intelligent advertising; Bidding model; Feature selection; Accuracy enhancement
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How to cite this paper
Feature Selection and Accuracy Enhancement Methods for Intelligent Advertising Bidding and Ranking Models
How to cite this paper: Taige Zhang. (2025) Feature Selection and Accuracy Enhancement Methods for Intelligent Advertising Bidding and Ranking Models. Advances in Computer and Communication, 6(4), 178-182.
DOI: http://dx.doi.org/10.26855/acc.2025.10.004
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