# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 1 Introduction  
Nowadays, the rapid growth of information aggravates the problem of information overload. Collaborative recommender systems (CRS) [1], which can filter out the information that users are interested in according to the user profiles, are designed to deal with this problem. They have been successfully applied in many fields, such as product sales ([[https://www.amazon.com/](https://www.amazon.com/)]([https://www.amazon.com/](https://www.amazon.com/)) (accessed on 15 July 2022)), multimedia services ([[https://www.netflix.com/](https://www.netflix.com/)]([https://www.netflix.com/](https://www.netflix.com/)) (accessed on 15 July 2022)), and so on. Open nature is an essential characteristic of CRS. For this reason, the CRS are vulnerable to recommendation attack [2, 3, 4]. Fake profiles are injected into the rating database of CRS by attackers to influence the recommendation results in the recommendation attack. Fake profiles are usually called attack profiles. The recommendation attack with the purpose of promoting or demoting the target item is called push and nuke attack [5], respectively. At the beginning, attack models [5] are used to construct attack profiles. Random, average, and bandwagon [2, 6, 7] attacks are three representative attack models. Rich attack profiles with different attack strength can be obtained by setting the attack strategy, attack size, and filler size of the attack model [5]. In recent years, attack profiles in the real scene are labeled from the Amazon Review dataset [8]. This work further enriches the mode of the recommendation attack. To detect the attack, a lot of unsupervised [9; 10; 11; 12; 13; 14; 15; 16], semi-supervised [17; 18], and supervised [19; 20; 21; 22; 23; 24; 25; 26; 27] detection methods have been proposed. One advantage of unsupervised detection methods is that labeled user profiles are not required for the detection. It is for this reason that unsupervised detection methods need some prior knowledge or some assumptions to perform the detection. For example, PCA-based detection method [10] takes the attack size as a prior knowledge. Clustering-based detection methods [12; 13; 14; 15; 16] usually assume that the test sets contain both genuine and attack profiles. However, above prior knowledge is usually hard to get and above assumption is not always true in real application. Semi-supervised detection methods first train weak classifiers with a few labeled user profiles. Then, some unlabeled user profiles are used to improve the weak classifiers. This type of method reduces the dependence on prior knowledge and assumptions. However, existing semi-supervised detection methods need extract hand-designed features of recommendation attack which is a challenging task even for domain experts. Supervised detection methods usually have good detection performance by training the classifiers with labeled user profiles. They learn knowledge from the training samples instead of depending on prior knowledge or assumptions for the detection. In particular, the recently proposed deep learning-based detection methods [23; 24; 25; 26; 27] do not need the hand-designed features and can automatically learn the features of recommendation attack besides having excellent detection performance. In the deep learning-based detection methods, there are usually many hyperparameters need to be set. These hyperparameters can greatly affect the detection performance of the detection methods. Although, some of the hyperparameters such as learning rate, loss function, and so on can be well determined by referring to relevant research results, the remaining hyperparameters such as activation function, epochs, and so on, which are called _key hyperparameters_ for ease of discussion, need to be optimized differently for different CRS. In most of the existing deep learning-based detection methods, however, the key hyperparameters are optimized by manual analysis.