Hybrid Recommendation System Based on Collaborative and Content-Based Filtering

Abstract

A Recommendation System (RS) is a method which filters the information and helped users’ to choose the corresponding target from the huge amount of information obtainable in online. The system recommends useful and satisfactory products (items) such as books, music, jokes, and movies for targeting users based on their interest. The content-based filtering as well as collaborative are different systems used often while designing the RS that predicts the recommended item(s) based upon the user preferences. However, the collaborative filtering algorithm provides poor performance for data sparsity, and it is complex for tracking the change of user interest. Moreover, the hybrid system has combined both the techniques in multiple ways to overcome the shortcomings and optimize the outcomes. Thereby, this article plans to develop a new hybrid recommender system assisting with the optimization concept for optimal recommendation list based on user preference or interest. At first, the feature extraction process takes place, in which the content features and the collaborative features are extracted based on (a) profile construction, (b) content similarity index, (c) Neighbor finder, (d) Items generator, and (e) Items weight generator and variance generator. Consequently, the optimal recommendation is carried out on the basis of features extracted. Further, the developed work plans to carry out the optimal rating of recommendation using a FireFly with Weighted Crow Search Algorithm (FF-WCSA). At last, the outcomes of the developed model is computed to extant approaches in terms of various metrics like accuracy, FDR, MAE, MARE, MSE, MSRE, RMSE, and RMSRE, respectively.