# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 2 Literature Review  
This section discusses the state-of-the-art techniques used in recommendation systems. The collaborative filtering technique-based recommendation algorithms are discussed first, which is followed by the deep learning techniques for recommendation. The collaborative filtering technique considers historical data to recommend items to the target users. It is mainly categorized into memory-based and model-based techniques. Out of these two, the memory-based approach is most popular and is subdivided into user-based [18] and item-based [19] approaches. Many similarity measures, i.e., Modified Proximity Impact Popularity (MPIP) [20], RJaccard [21], TMJ [22], etc., have been introduced in recent years. The CF approach is popular because it is simple to implement; nonetheless, it suffers from a data sparsity problem. Many model-based algorithms, such as Bayesian models [23], Latent Semantic models [24], Regression-based models [25], Matrix Factorization models [26], etc., have been proposed to overcome the challenge mentioned above. Among these models, the most prominent is Matrix Factorization, which maps the users and items into vectors representing the users' or items' latent features. Some variants of the Matrix Factorization (MF) model are Nonparametric Probabilistic Principal Component Analysis (NPCA) [27], Singular Value Decomposition (SVD) [28], and Probabilistic Matrix Factorization (PMF) [29]. The latent features learned by matrix factorization approaches are ineffective when the rating matrix is highly sparse. In addition, numerous researchers construct an effective recommendation system using additional data, such as time, trust, and location information. For instance, the authors in [30] utilized the user's trust and location information to improve the prediction accuracy of RS. Some researchers incorporated time information at the similarity computation [31], prediction [32], or rating matrix levels [33] of the recommendation process. On the other hand, deep learning [34] is a growing field of machine learning research that mimics the human brain's behavior for interpreting data such as images, sounds, and texts. It is a Multi-Layer Perceptron with multiple hidden layers stacked on each other. [PERSON] et al. [35] introduced the concept of Deep Learning and proposed an unsupervised algorithm based on deep belief networks (DBN). These Deep Learning techniques have recently succeeded in many complex tasks, i.e., computer vision, natural language processing, etc. Therefore, researchers have begun to apply Deep Learning algorithms to recommending music [36], books [11], videos [16], medical [37], interfaces [38], etc. The first Deep Learning-based recommendation model was Neural Collaborative Filtering (NCF) [39], which targets the poor representation of MF in a low-dimensional space and replaces it with neural network architecture. Since MF is a popular technique used in RS, several works have been proposed using Matrix Factorization and Deep Neural Networks. [PERSON] et al. [40] proposed a Deep Matrix Factorization (DMF) model in which features are extracted from the user-item matrix and integrated into the neural network. The author also proposed a new loss function based on binary cross-entropy to minimize the error. A new approach based on personalized Long and Short-Term Memory (LSTM) and Matrix Factorization techniques is presented in [41]. In [42], the authors developed an improved matrix factorization technique. A movie recommendation system that predicted the Top-N recommendation list of movies using singular value decomposition and cosine similarity is proposed in [43]. In the Deep Learning model for a Collaborative Recommender System (DLCRS) [44], user and movie IDs are concatenated as one-hot vectors. Unlike the basic MF approach, where inner products combine the user and movie ratings, DLCRS performs the element-wise multiplication of user and movie ratings.