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.

翻译：特征选择作为推荐系统中筛选关键特征的技术，正受到越来越多的研究关注。近期，自适应特征选择（AdaFS）通过为每个数据实例自适应地选择特征展现出卓越性能，该方法考虑到给定特征域的重要性在不同数据间可能存在显著差异。然而，该方法仍存在局限性：其选择过程易偏向高频出现的常见特征。针对这些问题，我们提出多视角特征选择（MvFS），该方法能更有效地为每个实例选择信息性特征。最重要的是，MvFS采用由多个子网络构成的多视角网络，每个子网络学习对具有不同特征模式的部分数据测量特征重要性。通过这种方式，MvFS缓解了主导模式的偏差问题，并促进更均衡的特征选择过程。此外，MvFS采用了一种有效的特征重要性评分建模策略，该策略独立作用于每个特征域，避免特征间产生依赖性。在实际数据集上的实验结果表明，与当前最先进的基线方法相比，MvFS具有显著有效性。