SlowGuess/NeurIPS_2023_OCR huggingface dataset:
From the visualization, we can observe that OptFeature acts as a hybrid approach, exhibiting a combination of both field-level and value-level interactions. Interestingly, we note significant differences between certain interaction fields in the KDD12 and Avazu datasets. OptFeature-f retains all of its interactions, while OptFeature-v only keeps a proportion of the value-level interactions. This observation further emphasizes the importance of exploring interactions at a finer-grained level.
C.5 Experiment on Industrial Dataset
We explicitly conduct experiments on an industrial large-scale dataset, which contains more feature fields and fewer feature values compared with previous benchmarks. The following results in Table 5 can further prove the effectiveness of OptFeature. We can make the following observations. Firstly, OptFeature outperforms other models on the industrial dataset, validating the effectiveness of our hybrid-grained selection space and selection algorithm. This observation is consistent with those on the other three public benchmarks, as shown in Table 1. Secondly, AutoFIS performs superiorly compared to other baselines. This confirms the significance of conducting feature interaction selection within DSNs. Lastly, PROFIT is surpassed by AutoFIS, unlike on previous public datasets. We believe this is likely due to the increasing number of feature fields. AutoFIS, which treats each field independently, can extend easily to datasets with more feature fields. AutoFIS, which decomposes the feature selection result on the field level, does not generalize well. With the increase of the feature fields, the complexity of such decomposition increases exponentially. OptFeature, on the contrary, can potentially extend to datasets containing more fields better due to its hybrid selection granularity.
Table 5: Performance Comparison on Industrial Dataset
Dataset	Industrial
Category	Model	AUC	Logloss
Shallow	LR	0.7745	0.2189
FM	0.7780	0.2181
DSNs	FNN	0.7838	0.2168
DeepFM	0.7824	0.2179
DCN	0.7844	0.2167
IPNN	0.7883	0.2147
DSNs with FIS	AutoFIS	0.7889	0.2146
PROFIT	0.7849	0.2161
OptFeature	0.7893	0.2142
The best and second best performed results are marked in bold and underline format respectively.
Successfully identifying informative feature interactions could be a double-edged sword. On the one hand, by proving that introducing noisy features into the model could harm the performance, feature interaction selection could be used as supporting evidence in preventing certain businesses, such as advertisement recommendations, from over-collecting users' information, thereby protecting user privacy. On the other hand, these tools, if acquired by malicious people, can be used for filtering out potential victims, such as individuals susceptible to email fraud. As researchers, it is crucial for us to remain vigilant and ensure that our work is directed towards noble causes and societal benefits.