DSPCNN: Deep Spiking Parallel Convolutional Neural Network for Video Recommendation System

The recommendation system infers the interest of users by collecting their behaviors and producing the recommendation list. A video recommender system aims to satisfy the requirements of users based on their interests. Many traditional video recommendation systems rely on the linear relationship between users and their neighbors, often overlooking higher-order relationships among users, which can negatively affect recommendation accuracy. Here, a Deep Spiking Parallel Convolutional Neural Network (DSPCNN) is developed for a video recommendation system. Initially, the user-video accumulation matrix is calculated. Thereafter, the video grouping is conducted employing the Fuzzy Local Information Cluster means (FLICM). Subsequently, bi-level matching between the user query and the video group is performed using squared-chord distance and Matusita distance, allowing for the retrieval of the user-preferred video. Finally, the video recommendation is performed using the proposed DSPCNN. Moreover, the merging of Spiking Deep Residual Network (S-ResNet) and Parallel Convolutional Neural Network (PCNN) creates the DSPCNN. Here, the video having maximum reach metrics is considered as the recommended one, which is newly developed based on click rate, number of likes, follows, comments, and forwards. Finally, the recommended video is provided to the users. It can be identified that DSPCNN has obtained an accuracy of 91%, a mean squared error (MSE) of 0.070, and a root mean squared error (RMSE) of 0.265. The source code is “https://github.com/SravaniAndavar/DSPCNN.git.”