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

  • Shruti Tomer
  • Sushama Nagpal
  • Simran Kaur Bindra
  • Vipra Goel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

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).

Keywords

Recommender Systems Computational intelligence techniques Gravitational Search Algorithm Fuzzy sets Particle Swarm Optimization Collaborative Filtering Hybrid filtering 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Samsung India Electronics Pvt LtdNoidaIndia
  2. 2.Netaji Subhas Institute of TechnologyDwarka, DelhiIndia
  3. 3.Adobe Systems India Pvt LtdNoidaIndia
  4. 4.Expedia Online Travel Svc Pvt LtdGurugramIndia

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