In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -collaborative filtering -on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural networkbased Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

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Consistent with theoretical predictions, we find that both a higher level of financial leverage and a faster speed of adjustment of leverage toward the shareholders' desired level are associated with better corporate governance quality as defined by a more independent board featuring CEO-chairman separation and greater presence of outside directors, coupled with larger institutional shareholding. In contrast, managerial incentive compensation on average discourages use of debt or adjustments toward the shareholders' desired level, consistent with its entrenchment effect. The effect of corporate governance on leverage adjustments is most pronounced when initial leverage is between the manager's desired level and the shareholders' desired level where the interests of managers and shareholders conflict.