Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction

3 Aug 2021  ·  Fuyuan Lyu, Xing Tang, Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, Xue Liu ·

Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and item features. As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models. Therefore, effectively modelling feature interactions has attracted much attention in both the research and industry field. The current approaches can generally be categorized into three classes: (1) na\"ive methods, which do not model feature interactions and only use original features; (2) memorized methods, which memorize feature interactions by explicitly viewing them as new features and assigning trainable embeddings; (3) factorized methods, which learn latent vectors for original features and implicitly model feature interactions through factorization functions. Studies have shown that modelling feature interactions by one of these methods alone are suboptimal due to the unique characteristics of different feature interactions. To address this issue, we first propose a general framework called OptInter which finds the most suitable modelling method for each feature interaction. Different state-of-the-art deep CTR models can be viewed as instances of OptInter. To realize the functionality of OptInter, we also introduce a learning algorithm that automatically searches for the optimal modelling method. We conduct extensive experiments on four large datasets. Our experiments show that OptInter improves the best performed state-of-the-art baseline deep CTR models by up to 2.21%. Compared to the memorized method, which also outperforms baselines, we reduce up to 91% parameters. In addition, we conduct several ablation studies to investigate the influence of different components of OptInter. Finally, we provide interpretable discussions on the results of OptInter.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Click-Through Rate Prediction Avazu OptInter AUC 0.8062 # 2
LogLoss 0.3637 # 1
Click-Through Rate Prediction Avazu OptInter-M AUC 0.8060 # 3
LogLoss 0.3638 # 2
Click-Through Rate Prediction Criteo OptInter AUC 0.8101 # 10
Log Loss 0.4417 # 4
Click-Through Rate Prediction iPinYou OptInter-M AUC 0.7800 # 4
LogLoss 0.00564 # 2
Click-Through Rate Prediction iPinYou OptInter AUC 0.7825 # 3
LogLoss 0.005604 # 1


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