Scope of context awareness in cross domain recommender system – a brief review

  • Authors

    • Kala K U Pondicherry university
    • M Nandhini Pondicherry university
    2019-04-07
    https://doi.org/10.14419/ijet.v7i4.19108
  • Cross Domain Recommender Systems, Context Aware Recommender Systems, Cross Domain-Context Aware Recommender System (CDCARS), Multi Domain RS, Contextual Modeling, Evaluation Metrics.
  • Cross Domain Recommender Systems (CDRS) and Context Aware Recommender systems (CARS) are the major emerging and fast growing research topics in the active research field of Recommender Systems. For personalized recommendation, CARS utilizes different contexts in a particular domain along with user ratings, whereas CDRS utilizes data from one or more domains to make predictions to the users either one of the domains by using utilizing the context similarity among those domains. These research areas are still new and largely unexplored. Here we are surveying different researches happened in each field of Recommender System(RS) separately and thus tries to find out the scope of combining them to solve the state of the art problems in RS research and the possibilities of improving the efficiency and accuracy of RS. CDRS is emphasized mainly only the historical data of both source and target domains only, but the thing is that users choice may change according to different temporal contexts such as time, location etc. Both can complement each other for the betterment of recommendation tasks. As a result of this survey, an outline of the framework is proposed for Cross Domain-Context Aware Recommender System (CDCARS).

     

     

     


  • References

    1. [1] Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734–749, 2005 https://doi.org/10.1109/TKDE.2005.99.

      [2] M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,†Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997 https://doi.org/10.1145/245108.245124.

      [3] Park, Young. "Advanced Recommender Systems." Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018. 1735-1745. Web. 21 May. 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch151.

      [4] A. Dey, G. Abowd, and D. Salber, “A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications,†HumAN-Computer Interaction, vol. 16, pp. 97-166, Dec. 2001 https://doi.org/10.1207/S15327051HCI16234_02.

      [5] I. Fernández-Tobías, I. Cantador, M. Kaminskas and F. Ricci, “Crossdomain recommendr sysems: A servey of the State of the Artâ€, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain and Faculty of Computer Science,Free University of BozenBolzano, 39100 Bolzano, Italy, 2012

      [6] B. Schilit, N. Adams, and R. Want ―Context-Aware Computing Applications‖ In WMCSA ’94: Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, IEEE Computer Society, pp. 85–90, Washington, DC, USA, 1994.

      [7] G. Chen and D. Kotz ―A Survey of Context-Aware Mobile Computing Research‖ Technical Report, Hanover, NH, USA, 2000.

      [8] A. Schmidt, M. Beigl, and H.-W. Gellersen ―There is More to Context than Location‖, Computers & Graphics, 23(6):893 – 901, 1999 https://doi.org/10.1016/S0097-8493(99)00120-X.

      [9] A. Zimmermann, A. Lorenz, and R. Oppermann ―An Operational Definition of Context‖ In Proceedings of the 6th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT’07, Springer-Verlag, Berlin, Heidelberg, pp. 558–571, 2007. https://doi.org/10.1007/978-3-540-74255-5_42.

      [10] Swapna Joshi, Kashibai Navale and Manisha Patil -- Enhanced Cross Domain Recommender System using Contextual parameters in Temporal Domain, International Journal of Computer Applications Technology and Research Volume 6–Issue 8, 355-361, 2017, ISSN: -2319–8656

      [11] Silva, Douglas Veras da, Ricardo B. C. Prudêncio, Carlos Ferraz, Alysson Bispo and Thiago Monteiro Prota. “Context-Aware Techniques for Cross-Domain Recommender Systems.†2015 Brazilian Conference on Intelligent Systems (BRACIS) (2015): 282-287.

      [12] Yuan Z., Yu K., Zhang J., Pan H. (2012) Structural Context-Aware Cross Media Recommendation. In: Lin W. et al. (eds) Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg https://doi.org/10.1007/978-3-642-34778-8_74.

      [13] M. Kaminskas, I. Fernandez-Tob ´ ´ıas, I. Cantador, and F. Ricci, “Ontology-based identification of music for places,†in 13th International Conference on Information and Communication Technologies in Tourism. Springer, 2013

      [14] Schedl, Markus, Zamani, Hamed, Chen, Ching-Wei, Deldjoo, Yashar, and Elahi, Mehdi, Current Challenges and Visions in Music Recommender Systems Research, Information Retrieval, 2018, arXiv:1710.03208 [cs.IR]

      [15] Ivan Cantador & Cremonesi, Paolo. (2014). Tutorial on cross-domain recommender systems. RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems. 401-402. 10.1145/2645710.2645777.

  • Downloads

  • How to Cite

    K U, K., & Nandhini, M. (2019). Scope of context awareness in cross domain recommender system – a brief review. International Journal of Engineering & Technology, 7(4), 5570-5579. https://doi.org/10.14419/ijet.v7i4.19108