Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )
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Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation.
Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.
Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions.
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
After grouping deterministic actions together, we construct a novel sequential path, which elaborately depicts the post-click behaviors of users.
The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.
Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.
In most current DNN based models, feature embeddings are simply concatenated for further processing by networks.