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An accurate estimation of the click-through rate (CTR), that is, the probability of clicking on a recommended advertisement item online, is crucial for advertising agencies to make appropriate suggestions to users and maximise their income. However, the existing CTR prediction techniques exhibit two major limitations in estimating accurate CTRs. First, they lack interpretability as they are unable to justify and explain the outcomes of the models. Irrelevant recommendations of products and services may have severe consequences on the personal and financial safety of a user. Second, the high computational complexity of deep-learning models hinders deployment in real-time recommender systems. Thus, this study proposes an interpretable, accurate, and efficient CTR estimator based on the neural additive factorization model (NAFM). The NAFM leverages the model distillation framework, where it effectively learns from a gated ensemble of existing CTR models. The NAFM provides interpretable insights into features and their interactions with a reduced computational cost. The effectiveness and efficiency of the proposed NAFM was verified by applying it on two public click-through datasets, namely, Criteo and Avazu. Unlike post-hoc explainable models, the NAFM avoids an additional phase of training to generate an interpretable model, and its explanations are truthful to the model. [ABSTRACT FROM AUTHOR]

This article is concerned with the resilient fault diagnosis (FD) problem for a class of complex networks subject to possible loss of the actuator effectiveness and random variation of the filter gain. An unknown diagonal matrix is employed to characterize the multiplicative loss of actuator effectiveness for each node. The proposed filter utilizes the information from only the local node and the neighboring nodes. Since there is no need to have a center node receiving global information from every node, the developed FD algorithm is truly distributed. In the presence of gain variations, a time-varying filter is constructed to jointly estimate the system state and the loss of actuator effectiveness at each node. An upper bound of the filtering error covariance is calculated and then minimized via appropriately determining the filter gains. The filter is designed by solving two sets of recursive matrix equations, thereby meriting the suitability of online applications. Sufficient conditions are established to guarantee the exponential boundedness in mean square of the filtering error, and the monotonicity of the estimation error covariance with respect to the coupling strength is also investigated. An illustrative example is provided to show the usefulness of our FD strategy. [ABSTRACT FROM AUTHOR]