Papers › Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Weiyu Cheng, Yanyan Shen, Linpeng Huang
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Click-Through Rate Prediction | Avazu | AFN+ | AUC | 0.7555 | #14 of 15 | Archive leaderboard | report |
| Click-Through Rate Prediction | Criteo | AFN+ | AUC | 0.8074 | #24 of 39 | Archive leaderboard | report |
| Click-Through Rate Prediction | Frappe | AFN+ | AUC | 0.9783 | #5 of 5 | Archive leaderboard | report |
| Click-Through Rate Prediction | MovieLens | AFN+ | AUC | 0.95 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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