Selection of Similarity Function for Context-Aware Recommendation Systems

Earlier recommendation engines used to work on just the user and item details; however, more recently, users' specific contexts have found an equally significant place as a metric in finding recommendations. The addition of contexts to the mix makes for more personalized suggestions and search for a truly efficient context-aware recommendation system (CARS) continues. Differential context weighting (DCW)-based CARS needs to compute similarities between different users to give recommendations. Our objective is to analyze, compare, and contrast various similarity functions, not only to find the best-suited one but also to implement an efficacious and economic CARS. To optimize the weights in DCW, we use a metaheuristic approach.