# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
###### Abstract  
In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users' tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model's performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches. collaborative filtering; deep neural network; noisy ratings; recommendation system; time decay functions; matrix factorization +
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which increases with the rapid increase in users and items. More importantly, it cannot generate recommendations for a new item that has not received user ratings. Therefore, many researchers have begun to look for other ways to improve the recommendation system performance. Demographic-based RS [7] provides recommendations based on the demographic profiles of users' attributes, i.e., user's country, education, gender, or age. This technique is not complex and easy to implement, but profiles are rarely updated. In Knowledge-Based RS (KBRS) [8], item recommendations are based on the domain knowledge about a user's needs and preferences. As the recommendations are independent of the user's preferences, a KBRS can quickly change its recommendations when a user's interest change. The limitation of KBRS is that it must understand the product domain well. Community-based RS [7] suggests items based on the preferences of the user's friends. A hybrid recommendation system combines two or more techniques, as discussed above. In recent years, Deep Learning (DL) [9] has made significant progress and achieved notable success in many sectors such as healthcare [10], cybersecurity [10], natural language processing [11], audio recognition [11], computer vision [12], etc. The promising capabilities of DL have encouraged researchers to use deep architecture for recommendation tasks [13; 14]. For instance, [PERSON] et al. [15] introduced a deep architecture for google play recommendations, [PERSON] et al. [16] proposed a deep learning-based method for the YouTube recommendations, and [PERSON] et al. [17] used a Recurrent Neural Network for news recommendations. These approaches have displayed remarkable success and outperformed traditional RS methods. Developing personalized RS using the Deep Neural Network (DNN) has become a potential trend due to its high computing power and huge data storage capabilities. The simplest DNN [13] consists of three layers, i.e., the input layer, the Multi-Layer Perceptron (MLP), and the output layer. The input layer converts low-dimensional features into N-dimensional features and passed them to the MLP layers. In MLP, n-dimensional features are learned and passed onto the activation functions of each layer. Finally, the MLP's output is passed to the last layer, which predicts the ratings. Some researchers have recently developed Deep Learning-based recommendation models to improve the system's accuracy.