Light Fidelity(LiFi)- Wireless Optical Networking Technology

EOI: 10.11242/viva-tech.01.04.243

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Mr. Yashit Yadnesh Save ,Prof. Nitesh Kumar, "Recommender Systems", VIVA-IJRI Volume 1, Issue 4, Article 243, pp. 1-6, 2021. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.


advice system,filtering, metadata, recommender system, software


  1. J.A. Konstan, j. RiedlRecommender systems: from algorithms to consumer experience,Consumer model person-adapt engage, 22 (2012), pp. One zero one-123Crossrefview file in scopusgoogle pupil
  2. C. Pan, w. LiResearch paper advice with subject matter evaluationIn pc layout and programs ieee, four (2010)Pp. V4-264,Pu P, Chen L, Hu R. A user-centric evaluation framework for recommender systems. In: Proceedings of the fifth ACM conference on Recommender Systems (RecSys’11), ACM, New York, NY, USA; 2011. p. 57–164.
  3. Hu R, Pu P. Potential acceptance issues of personality-ASED recommender systems. In: Proceedings of ACM conference on recommender systems (RecSys’09), New York City, NY, USA; October 2009. p. 22–5.
  4. B. Pathak, R. Garfinkel, R. Gopal, R. Venkatesan, F. Yin,Empirical analysis of the impact of recommender systems on salesJ Manage Inform Syst, 27 (2) (2010), pp. 159-188View Record in ScopusGoogle Scholar
  5. Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA et al. Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the international conference on intelligent user interfaces; 2002. p. 127–34.
  6. J. Beel, S. Langer, A. Nürnberger, and M. Genzmehr, “The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems,” in Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), 2013, pp. 400–404.
  7. W. Böhm, A. Geyer-schulz, M. Hahsler, and M. Jahn, “Repeat-Buying Theory and Its Application for Recommender Services,” in Proceedings of the 25th Annual Conference of the GesellschaftfürKlassifikatione.V., 2003, pp. 229–239.
  8. M. Baez, D. Mirylenka, and C. Parra, “Understanding and supporting search for scholarly knowledge,” in Proceeding of the 7th European Computer Science Summit, 2011, pp. 1–8.
  9. J. Beel, B. Gipp, S. Langer, and M. Genzmehr, “Docear: An Academic Literature Suite for Searching, Organizing and Creating Academic Literature,” in Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2011, pp. 465–466.
  10. J. Beel, B. Gipp, and C. Mueller, “SciPloreMindMapping’ - A Tool for Creating Mind Maps Combined with PDF and Reference Management,” D-Lib Magazine, vol. 15, no. 11, Nov. 2009.
  11. S. Bethard and D. Jurafsky, “Who should I cite: learning literature search models from citation behavior,” in Proceedings of the 19th ACM international conference on Information and knowledge management, 2010, pp. 609–618.
  12. T. Bogers and A. van den Bosch, “Recommending scientific articles using citeulike,” in Proceedings of the 2008 ACM conference on Recommender systems, 2008, pp. 287–290. 46 [14] K. D. Bollacker, S. Lawrence, and C. L. Giles, “CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications,” in Proceedings of the 2nd international conference on Autonomous agents, 1998, pp. 116–123.
  13. J. Bollen and H. Van de Sompel, “An architecture for the aggregation and analysis of scholarly usage data,” in Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, 2006, pp. 298–307.
  14. T. CiteSeerX, “About RefSeer,”, 2012.
  15. CiteULike, “My Top Recommendations,” Website, 2011.