Review of Recommender systems

This is an example of term papers from one of the former students. Use this example as tip, not as your guidance for your project. We provide some part of chapters and references to help you design your project or paper. Do not limit your project or paper with the example. Work on your idea and address your findings.

Classification of Recommendation Systems

  • Traditional Recommendation Systems
    • Collaborative Methods
    • Content-Based Method
    • Hybrid Methods
  • Social Recommender Systems
    • Memory Based (e.g. TidalTrust, MoleTrust, SoRec)
    • Model Based

  • Mobile Recommendation Systems

  • Context-Aware Recommendation Systems

Use of recommentation systems in the Industry

There are some examples:

  • Amazon Recommendation System
  • YouTube Recommendation System
  • Netflix Recommendation System
  • Google News Recommendation System

References

  • Ma, Hao, et al. “Recommender systems with social regularization.” Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011
  • Adomavicius, Gediminas, and Alexander Tuzhilin. “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions.” Knowledge and Data Engineering, IEEE Transactions on 17.6 (2005): 734-749.
  • Ricci, Francesco. “Mobile recommender systems.” Information Technology & Tourism 12.3 (2010): 205-231.
  • http://en.wikipedia.org/wiki/Recommender_system
  • http://fortune.com/2012/07/30/amazons-recommendation-secret/
  • Melville, Prem, and Vikas Sindhwani. “Recommender systems.” Encyclopedia of machine learning. Springer US, 2010. 829-838.
  • John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July 1998
  • Greg Linden, Brent Smith, and Jeremy York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing,
  • Das, Abhinandan S., et al. “Google news personalization: scalable online collaborative filtering.” Proceedings of the 16th international conference on World Wide Web. ACM, 2007.
  • King, Irwin, Michael R. Lyu, and Hao Ma. “Introduction to social recommendation. “Proceedings of the 19th international conference on World wide web. ACM, 2010.
  • Golbeck, J.: Generating predictive movie recommendations from trust in social networks. Springer (2006)
  • Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 17–24. ACM (2007)
  • Ma, N., Lim, E., Nguyen, V., Sun, A., Liu, H.: Trust relationship prediction using online product review data. In: Proceeding of the 1st ACM international workshop on Complex networks meet information & knowledge management, pp. 47–54. ACM (2009)
  • Tang, Jiliang, Xia Hu, and Huan Liu. “Social recommendation: a review.” Social Network Analysis and Mining 3.4 (2013): 1113-1133.
  • Davidson, James, et al. “The YouTube video recommendation system.” Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
  • Adomavicius, Gediminas, and Alexander Tuzhilin. “Context-aware recommender systems.”Recommender systems handbook. Springer US, 2011. 217-253.
  • Reddy, Sasank, and Jeff Mascia. “Lifetrak: music in tune with your life.” Proceedings of the 1st ACM international workshop on Human-centered multimedia. ACM, 2006