Learning effective high-order feature interactions is crucial for click through rate(CTR)prediction in recommender systems.Existing methods that learn meaningful high-order feature combinations by reassembling low-order feature combinations,i.e.,2-order feature interaction,suffer from high computational costs to calculate the interaction weight of all pairwise feature interactions.Some deep neural network-based methods can be seen as universal function approximators to potentially learn all kinds of feature interactions.However,it had been proved to be inefficient to approximate the low-order interactions,i.e.,2-order or 3rd-order feature interactions,which may influence the accuracy of CTR prediction task.Based on the above consideration,we propose a multi-granularity based feature interaction pruning network(FeatNet)for CTR prediction task.Firstly,FeatNet generates different subsets with a threshold pruning operation to select the meaningful feature combinations on the explicit feature granularity,which enables FeatNet to keep the diversity of different feature combinations,and reduce the complexity of high-order feature interactions.Based on the pruned feature subsets,implicit high-order feature interactions are further conducted on the granularity of feature elements,which automatically filters out the invalid feature interactions.Extensive experiments are conducted on two real-world datasets,showing the superiority of FeatNet in CTR prediction.
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