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As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi‐model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one‐dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real‐world datasets, the proposed work exhibits substantial performance when compared to the existing methods.

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In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users' previous information about items such as books, music, movies, ideas, and so on. Memory-based models are generally referred to as similarity-based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory-based approach includes user-based CF (UCF) and item-based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user-item rating matrix. However, consequent to the ingrained sparsity of the user-item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user-item rating matrix. Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies in the last five years have been investigated to explore modern techniques with future opportunities. In this regard, the related literature on anomaly detection systems in network traffic has been discussed, with a variety of typical applications such as WSNs, IoT, high-performance computing, industrial control systems (ICS), and software-defined network (SDN) environments. Finally, we underlined diverse open issues to improve the detection of anomaly systems.