Call for Paper

CAE solicits original research papers for the December 2021 Edition. Last date of manuscript submission is November 30, 2021.

Read More

An Influential Recommendation System Usage for General Users

Nikhat Akhtar, Devendera Agarwal. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Nikhat Akhtar, Devendera Agarwal
10.5120/cae2016652315

Nikhat Akhtar and Devendera Agarwal. An Influential Recommendation System Usage for General Users. Communications on Applied Electronics 5(7):5-9, July 2016. BibTeX

@article{10.5120/cae2016652315,
	author = {Nikhat Akhtar and Devendera Agarwal},
	title = {An Influential Recommendation System Usage for General Users},
	journal = {Communications on Applied Electronics},
	issue_date = {July 2016},
	volume = {5},
	number = {7},
	month = {Jul},
	year = {2016},
	issn = {2394-4714},
	pages = {5-9},
	numpages = {5},
	url = {http://www.caeaccess.org/archives/volume5/number7/630-2016652315},
	doi = {10.5120/cae2016652315},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Recommender systems are extensively seen as an effective means to combat information overload, as they redound us both narrow down the number of items to choose. They are seen as assistance us make better decisions at a lower transaction cost. Hence, recommender systems have become omnipresent in e-commerce and are also increasingly used in services in different other domains both online and offline where the number of items exceeds our potentiality to consider them all individually. The research papers recommender systems are software applications or systems that help individual users to discover the most relevant research papers to their needs. These systems use filtering techniques to create recommendations. These techniques are categorized majorly into collaborative-based filtering, content-based technique, and hybrid algorithm. In addition, they assist in decision making by providing product information both personalized and non-personalized, summarizing community opinion, search research papers, and providing community critiques. As a result, recommender systems have been shown to ameliorate the decision.

References

  1. P. Gupta, A. Goel, J. Lin, A. Sharma, D.Wang, and R. B. Zadeh WTF; The who-to-follow system at Twitter, Proceedings of the 22nd international conference on World Wide Web, pp 505-514, 13 May 2013.
  2. H. Jafarkarimi, A.T.H. Sim and R. Saadatdoost A Naïve Recommendation Model for Large Databases, International Journal of Information and Educa tion Technology, June 2012.
  3. Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-Commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1–2), 115–153. doi:10.1023/A:1009804230409
  4. Lam, S. K. T., Frankowski, D., & Riedl, J. (2006). Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In G. Müller (Ed.), Proceedings of the 2006 International Conference on Emerging Trends in Information and Communication Security (ETRICS'06), LNCS 3995, 14–29. Berlin Heidelberg, Germany: Springer. doi:10.1007/11766155_2.
  5. Nikhat Akhtar, Prof. (Dr.) Devendera Agarwal, “A Literature Review of Empirical Studies of Recommendation Systems” International Journal of Applied Information Systems (IJAIS) USA , Volume 10, No. 2, Pages 6 – 14, December 2015, ISSN 2249 - 0868, Link http://www.ijais.org/archives/volume10/number2/ 839-2015451467, DOI : 10.5120/ijais2015451467.
  6. Y. Liang, Q. Li and T. Qian, “Finding Relevant Papers Based on Citation Relations”, Springer-Verlag Berlin Heidelberg, (2011), pp. 403–414.
  7. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58. doi:10.1145/245108.245121
  8. Neumann, A. W. (2007). Motivating and supporting user interaction with recommender systems. In L. Kovács, N. Fuhr, & C. Meghini (Eds.), Proceedings of the 11th European Conference on Research and Advanced Technology for Digital Libraries (ECDL'07), LNCS 4675, 428–439. Berlin Heidelberg, Germany: Springer. doi:10.1007/978-3-540-74851-9_36.
  9. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70. doi:10.1145/138859.138867.
  10. Schafer, J.B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web, LNCS 4321, 291–324. Berlin Heidelberg, Germany: Springer. doi:10.1007/978-3-540-72079-9_9.
  11. Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504.doi:10.1007/s11257-011-9118-4.
  12. Pommeranz, A., Broekens, J., Wiggers, P., Brinkman, W.-P., & Jonker, C. M. (2012). Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process. User Modeling and User-Adapted Interaction, 22(4–5), 357–397. doi:10.1007/s11257-011-9116-6.
  13. Buder, J., & Schwind, C. (2012). Learning with personalized recommender systems: A psychological view. Computers in Human Behavior, 28(1), 207–216. doi:10.1016/j.chb.2011.09.002.
  14. Cremonesi, P., Turrin, R., Lentini, E., & Matteucci, M. (2008). An evaluation methodology for collaborative recommender systems. In Proceedings of the 2008 International Conference on Automated Solutions for Cross Media Content and Multi-channel Distribution (AXMEDIS '08), 224–231. Washington, DC, USA: IEEE Computer Society.doi:10.1109/AXMEDIS.2008.13.
  15. C. Wang and D. M. Blei, “Collaborative Topic Modeling for Recommending Scientific Articles”, In Proc. of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2011), pp. 448–456.
  16. C. Nascimento, A. H. F. Laender, A. S. da Silva and M. A. Gonçalves, “A Source Independent Framework for Research Paper Recommendation”. ACM, (2011) June 13–17, Ottawa, Ontario, Canada.
  17. Jeckmans, A. J. P., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R. L., & Tang, Q. (2013). Privacy in recommender systems. N. Ramzan, R. van Zwol, J.-S. Lee, K. Clüver, & X.-S. Hua (Eds.), Social Media Retrieval, 263–281. London, UK: Springer. doi:10.1007/978-1-4471-4555-4_12.
  18. Campochiaro, E., Casatta, R., Cremonesi, P., & Turrin, R. (2009). Do metrics make recommender algorithms? In Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops (WAINA '09), 648–653. Washington, DC, USA: IEEE Computer Society. doi:10.1109/WAINA.2009.127.
  19. Gedikli, F., & Jannach, D. (2010). Rating items by rating tags. In Proceedings of the 2nd Workshop on Recommender Systems and the Social Web (RSWEB'10), 25–32.
  20. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.doi:10.1016/j.knosys.2013.03. 012.
  21. Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., & Stumme, G. (2007). Tag recommendations in folksonomies. In J. N. Kok, J. Koronacki, R. Lopez De Mantaras, S. Matwin, D. Mladenič, & A. Skowron (Eds.), Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007), LNCS 4702, 506–514. Berlin Heidelberg, Germany: Springer. doi:10.1007/978- 3-540-74976-9_52.
  22. Konstan, J. A. (2012). Recommender Systems: The Power of Personalizatio Q&A. http://learning.acm.org/webinar/recommender_qa.cfm.
  23. Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209. http://www.misq.org/e-commerce-product-recommendation-agentsuse- characteristics-and-impact.html.
  24. Knijnenburg, B. P., Reijmer, N. J. M., & Willemsen, M. C. (2011). Each to his own: How different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (RecSys '11), 141–148. New York, NY, USA: ACM., doi:10.1145/2043932.2043960.
  25. Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web, LNCS 4321, 377–408. Berlin Heidelberg, Germany: Springer. doi:10.1007/978-3-540-72079- 9_12.
  26. Hangartner, R. (2007). What is the recommender industry? Mobile Groove.com. http://www.mobilegroove.com/guest-columnwhat-is-the-recommender-industry-750.
  27. Schafer, J.B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web, LNCS 4321, 291–324. Berlin Heidelberg, Germany: Springer. doi:10.1007/978-3-540-72079-9_9.
  28. Dourish, P., & Chalmers, M. (1994). Running out of space: Models of information navigation. Short paper presented at HCI'94, two pages. http://www.dcs.gla.ac.uk/~matthew/papers/hci94.pdf.
  29. Dieberger, A. (1997). Supporting social navigation on the World Wide Web. International Journal of Human-Computer Studies, 46(6), 805–825. doi:10.1006/ijhc.1996.0111.
  30. Konstan, J. A., & Riedl, J. (2012). Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction, 22, 101– 123. doi:10.1007/s11257-011-9112-x.
  31. Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., & Zadeh, R. (2013). WTF: The who to follow service at Twitter. In Proceedings of the 22nd International Conference on World Wide Web (WWW '13), 505–514. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. http://www2013.org/proceedings/p505.pdf.
  32. Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2013),” Do recommender systems manipulate consumer preferences? A study of anchoring effects,” SSRN. doi:10.2139/ssrn.2285042

Keywords

Recommendations System, Tagging, Information Retrieval, E-Commerce, Collaborative Filtering.