Fuzzy Gravitational Search Approach to a Hybrid Data Model Based Recommender System

Shruti Tomer, Sushama Nagpal, Simran Kaur Bindra, Vipra Goel

Abstract: In recent times, when the Internet is flooded with information, users get overwhelmed with the large amount of data and need some system to narrow down their choices. Recommender systems provide personalized suggestions to the users, giving them a better experience. Data Filtering methods along with many Computational Intelligence (CI) techniques have been used to build and optimize these systems. Here, we introduce a new Recommender System, based on Fuzzy Gravitational Search Algorithm using Hybrid Data Model (FGSA-HDM). FGSA-HDM uses a nature inspired heuristic technique, Gravitational Search Algorithm (GSA), to learn a user’s preference and optimize weightage given to different features which define the user profile. Also, to incorporate the fuzziness of human nature, these features have been represented by Fuzzy sets. The proposed technique, FGSA-HDM, has shown better results than the previously implemented techniques - Pearson Correlation based Collaborative Filtering (PCF), Fuzzy Collaborative Filtering (FCF), Fuzzy Genetic Algorithm based Collaborative Filtering (FG-CF) and Fuzzy Particle Swarm Optimization based Collaborative Filtering (FPSO-CF).