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CADCN: A click-through rate prediction model based on feature importance

Published: 27 July 2023 Publication History

Abstract

Recommendation systems are widely used in real-world advertising recommendations. In traditional recommendation system prediction models, click-through rate plays a crucial role. However, traditional recommendation systems cross-combine original features to make the linear model memorable and generalizable while taking into account the importance of features requires a lot of computational cost, and it uses manual cross-combination of features on data, which requires a lot of time and effort. Traditional recommendation systems simply learn the relationship between features without considering the importance of features. We combine deep crossover network and AFM network, dynamically assign weights to different features prior to feature crossover using a synthetic incentive network, and introduce attention mechanism based on feature crossover explicitly. We then propose an advertisement click-through rate prediction based on feature importance model, and the experimental results demonstrate that the algorithm is superior to the deep crossover network in predicting the click-through rate of advertisements.

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          CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things
          May 2023
          1025 pages
          ISBN:9798400700705
          DOI:10.1145/3603781
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 27 July 2023

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          Author Tags

          1. Click-through rate prediction
          2. factorization machine
          3. feature importance
          4. synthetic incentive network

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          • National Natural Science Foundation of China

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          CNIOT'23

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          Overall Acceptance Rate 39 of 82 submissions, 48%

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