The Design of Web Based Car Recommendation System using Hybrid Recommender Algorithm

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Web based recommendations for any item is mandatory in E-commerce based web sites. This paper is about the design of web based car recommendation system using the hybrid recommender algorithm. The proposed hybrid recommender algorithm is the combination of user-to-user and item-to-item collaborative filtering method to generate the car recommendations. The user model is designed using demographic features, click data and browsing history. Item profile is built using the various attributes of car, 40 brands of car including 224 car types are used in this work. The synthetic dataset of 300 users with 10000 sessions is used to build user model. The proposed algorithm is evaluated with 100 real time users and shows the 83% accuracy in generating recommendations.



  • Keywords

    Collaborative Filtering algorithm, Recommendation system, Item profile, User model, Hybrid recommender system

  • References

      [1] Kavinkumar.V, Rachamalla Rahul Reddy, Rohit Balasubramanian, Sridhar.M, Sridharan.K Dr. D.Venkataraman, “A Hybrid Approach for Recommendation System with Added Feedback Component”, IEEE, 2015.

      [2] Doychin Doychev, Aonghus Lawlor, Rachael Rafter, and Barry Smyth, “An Analysis of Recommender Algorithms for Online News”, Conference and Labs of the Evaluation Forum, 2014.

      [3] Zhuo Chen1,2, Yong Feng*1,2, Heng Li1,2, “A Novel Top-K Automobiles Probabilistic Recommendation Model using User Preference and User Community”, IEEE, 2014.

      [4] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Item-based Collaborative Filtering Recommendation Algorithms”, ACM, 2001.

      [5] Kyoung-jae Kima, Hyunchul Ahnb, “A recommender system using GA K-means clustering in an online shopping market”, ELSEVIER, 2007.

      [6] Tadashi Yanagihara, Ryosuke Namiki, Kazunari Nawa, David Weir, Kentaro Oguchi, “Combining Prediction Methods with Cyber Information for Proactive Route Recommendation”, IEEE, 2013.

      [7] Ranieri Baraglia and Fabrizio Silvestri, “An Online Recommender System for Large Web Sites”, IEEE, 2004.

      [8] Yong Soo Kima, Bong-Jin Yumb, “Recommender system based on click stream data using association rule mining”, ELSEVIER, 2011.

      [9] Gediminas Adomavicius, Alexander Tuzhilin, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions”, IEEE, 2005.

      [10] Wolfgang Woerndl, Christian Schueller, Rolf Wojtech, “A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications”, IEEE, 2007.

      [11] Robin Burke, “Integrating Knowledge-based and Collaborative-filtering Recommender Systems”, AAAI ,1999.

      [12] João Ferreira, Pedro Pereira, Porfírio Filipe, “Recommender System for Drivers of Electric Vehicles”, IEEE, 2011.

      [13] DanhuaiGuoa, YingqiuZhua, WeiXubc, ShuoShangd, ZhimingDinge, “How to find appropriate automobile exhibition halls: Towards a personalized recommendation service for auto show”, ELSEVIER, 2016.

      [14] Zan Huang, Wingyan Chung, Thian-Huat Ong, Hsinchun Chen, “A Graph-based Recommender System for Digital Library”, ACM, 2002.

      [15] Gediminas Adomavicius and Alexander Tuzhilin, “Multidimensional Recommender Systems: A Data Warehousing Approach”, SPRINGER, 2001.




Article ID: 16772
DOI: 10.14419/ijet.v7i3.4.16772

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.