Click Through Rate (CTR) prediction is a critical task in industrial applications, especially for online social and commerce applications. It is challenging to find a proper way to automatically discover the effective cross features in CTR tasks. We propose a novel framework for CTR task, called Deep neural networks with Encoder enhanced Factorization Machine (DeepEnFM). Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings by the clues of other fields. The embeddings generated from the encoder are beneficial for further feature interactions. Particularly, DeepEnFM utilizes a bilinear approach to generate different similarity functions with respect to different field pairs. Furthermore, the max-pooling method makes DeepEnFM feasible to capture both the supplementary and suppressing information among different attention heads. Our model is validated on the Criteo and Avazu datasets, and achieves the state-of-art performance.
The authors propose a model for Click-Through Rate Prediction using a model consisting of an embedding layer, a Transformer stack, a Factorization Machine, and a DNN. 
I have several major concerns about the submission:
2. Relevance: This work is extremely application specific, the application is not relevant to this community.
1. Clarity and writing: The contributions which are relevant to the ICLR community are not explained well and the paper needs copy-editing for English grammar
4. Novelty: While seemingly showing good results on some benchmarks, the model is a mix of many components and it's not clear which components actually improve performance and would be worth further study. 
Minor comments:
Applying the DNN directly on top of the embeddings, and having a parallel stack of Encoder-FM, is not well explained. What does it mean that "DNN aims at bit-wise level" if the DNN receives the same embedding features as the encoder, which supposedly "learn[s] at vector wise level"? 
References to datasets are missing
Ablation study is limited, and has surprising results. E.g. even completely removing self-attention barely makes a dent in how well the method compares to other published work, moving it from rank 1 to rank 2. Otherwise only small tweaks with even more minor effects are made. What about removing e.g. the FM, other major components?
The biggest architectural innovations here are the bi-linear attention mechanism and max-pooling self attention. They are hard to interpret in this context. It's not clear how they would perform in a simpler architecture (e.g. vanilla BERT or Transformer) and in the context of a more standard benchmark. That study would have a lot more relevance to this community than the present one.