SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine Intelligence

Video recommendation ensures that viewers get content more relevant to their choices and taste. With the aggregation of a diverse variety of content on video streaming platforms, there is a need to improve the existing recommendation model to increase accuracy in predicting the best content analogous to past choices of the user. The model proposed in this paper employs semantic similarity to generate recommendations based on metadata and annotations. The semantic inference-based model ensures the incorporation of cognitive knowledge of user inputs and past activities, yielding better insights into user preferences. The model utilizes web-sourced metadata to augment its dataset with supplementary information. The metadata is classified using RNN and is stored in a semantic network for feature selection. Topic modeling on query terms allows the discovery of hidden topics in the user query and the use of a knowledge store like DBpedia allows for enriching the query terms based on the topics discovered. The video dataset is categorized using a combination of Random Forest and decision tree-bagging techniques, which are based on the extracted features of the videos. The proposed SemVidRec model is compared with other video recommendation models like VAVR, PVRRC, CCVR, etc. SemVidRec was discovered to outperform the baseline models based on several performance metrics, achieving an accuracy of 96.45%, precision of 95.43%, and an nDCG value of 0.97.