  Feature selection, which is a technique to select key features in recommender
systems, has received increasing research attention. Recently, Adaptive Feature
Selection (AdaFS) has shown remarkable performance by adaptively selecting
features for each data instance, considering that the importance of a given
feature field can vary significantly across data. However, this method still
has limitations in that its selection process could be easily biased to major
features that frequently occur. To address these problems, we propose
Multi-view Feature Selection (MvFS), which selects informative features for
each instance more effectively. Most importantly, MvFS employs a multi-view
network consisting of multiple sub-networks, each of which learns to measure
the feature importance of a part of data with different feature patterns. By
doing so, MvFS mitigates the bias problem towards dominant patterns and
promotes a more balanced feature selection process. Moreover, MvFS adopts an
effective importance score modeling strategy which is applied independently to
each field without incurring dependency among features. Experimental results on
real-world datasets demonstrate the effectiveness of MvFS compared to
state-of-the-art baselines.
