MediaRec: A Hybrid Media Recommender System
Tanmay Bhuskute, Amit Jeve, Nihal Shah, Tejas Shah, B. A. Patil · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: This paper discusses about a hybrid recommendation platform for Movies, Books and Songs in one roof. A recommender system is a subgroup of information filtering systems that helps in predicting the “rating” or “Preference” that a user would give to any item. It also helps users to get media of their choice based on their experiences of self and other users in a productive and efficacious manner without wasting time in useless browsing. Previous approaches in recommender system (RS) include Content based filtering and Collaborative filtering. These approaches have a particular limitation as like the necessity of the user history as they visit. So as to overcome such dependencies, the Hybrid Recommendation System is introduced. It uses both Collaborative based filtering system and Content based filtering system for recommending media. In this way, the system performance will be greatly improved through the integration of these two. Keywords: Media Recommender System, Movies, Books, Songs, Recommender, TFIDF, Cosine Similarity, Pearson Correlation, KNN, K-Means Clustering.