A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Recommendation System into Social Life

  • Shawni Dutta
  • Prof. Samir Kumar Bandyopadhyay
Keywords: Item, Medicine, Product, Medical Application, Financial Application, Recommender System, Machine Learning

Abstract

Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the "rating" or "preference" of a user to an item.  In other words, both predict rating or preference for an item or product on a specific platform. The aim of the paper is to extend the areas of the recommender system/recommendation systems. The basic task of the recommender system mainly is to predict or analyze items/product. If it is possible to include more products in the system, then obviously the system may be extended for other areas also. For example, Medicine is a product and doctors filter the particular medicine for the particular disease. In the medical diagnosis doctors prescribed a medicine and it a product. It depends on the disease of the user/patient so here doctor predicts a medicine or product just like an item is recommended in a recommender system. The main objective of the paper is to extend the Recommender System/Recommendation system in other fields so that the research works can be extended Social Science, Bio-medical Science and many other areas.

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References

Gipp, B., Beel, J., & Hentschel, C. (2009, Jan). Scienstein: A research paper recommender system. In: Proceedings of the International Conference on Emerging Trends in Computing (ICETiC’09), pp. 309-315.

Sugiyama, K. & Kan, M. Y. (2010, Jun). Scholarly paper recommendation via user's recent research interests. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, pp. 29-38.

McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., & Riedl, J. (2002, Nov). On the recommending of citations for research papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, pp. 116-125.

Shardanand, U. & Maes, P. (1995, May). Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210-217.

Xia, F., Liu, H., Lee, I., & Cao, L. (2016). Scientific article recommendation: Exploiting common author relations and historical preferences. IEEE Transactions on Big Data, 2(2), 101-112.

Antenucci, S., Boglio, S., Chioso, E., Dervishaj, E., Kang, S., Scarlatti, T., & Dacrema, M. F. (2018). Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario. In: Proceedings of the ACM Recommender Systems Challenge 2018, pp. 1-6.

Aggarwal, C. C. (2016). Recommender systems. (Vol. 1). Cham: Springer International Publishing.

Published
2020-10-31
How to Cite
Shawni Dutta, & Prof. Samir Kumar Bandyopadhyay. (2020). A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Recommendation System into Social Life. International Journal of Engineering and Management Research, 10(5), 37-39. https://doi.org/10.31033/ijemr.10.5.9