Employability of the User Opinion in Developing a Product Recommender System
Armaan Jain
Ryan International School, Rohini-25, New Delhi
52-57
Vol: 10, Issue: 3, 2020
Receiving Date:
2020-07-09
Acceptance Date:
2020-08-25
Publication Date:
2020-09-15
Download PDF
http://doi.org/10.37648/ijrst.v10i03.007
Abstract
The development of the Internet has helped E-Commerce (web-based shopping). These days, web-based shopping is exceptionally famous with the increasing number of people associated with the Internet. Step by step, the interest in web-based shopping is additionally developing. The growing number of items over E-Commerce has made issues for the clients to buy the specific item simultaneously because of huge data. A recommender framework prescribes appropriate things to the clients from among the tremendous measures of information satisfying their taste, interest, and conduct. The paper presents an outline of the Recommender framework, its procedures with their deficiency and further, we proposed our system for item suggestion utilizing conclusions.
replica orologi di lusso
From the website, you can know more detailed information about 2023 perfect omega replica UK.
UK AAA replica uhren kaufen are equipped with Swiss movements. You can possess the cheap fake watches from online stores.
Keywords:
e-commerce; recommender system; Information search strategies
References
- Gediminas, A., and Alexander, T., “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”. replicas de relojes IEEE Transactions on Knowledge and Data Engineering, 17(16), pp. 734-749, 2005.
- Goldberg, D., Nichols, D., Oki, B., M., and Terry, D., “Using collaborative filtering to weave an information Tapestry”. Communications of the ACM, 25(12) pp. 61–70, 1992.
- Henry, L., A., 'Information search strategies on the Internet: A critical component of new literacies'. Webology, 2(1), Article 9, 2005.
- Shardanand, U., and Maes, P., “Social information filtering: Algorithms for automating “word of mouth”” in ACM CHI, ACM Press/Addison-Wesley Publishing Co. pp. 210-217, 1995.
- Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and evaluating choices in a virtual community of use,” ACM CHI, Press/Addison-Wesley Publishing Co, pp. 194–201, 1994.
- Nathanson, T., Bitton, E., and Goldberg, K., “Eigentaste 5.0: Constant-time adaptability in a recommender system using item clustering” Proceeding of the 2007 ACM Conference on Recommender System, pp. 149-152, 2007.
- Prem, M., and Vikas, S., “Recommender Systems”, Encyclopaedia of Machine Learning, springer, pp. 829-838, 2010.
- Michael, J., P., and Daniel, B., “Content-Based Recommendation Systems” The Adaptive Web: Methods and Strategies of Web Personalization, Springer, pp. 325-341, 2007.
- Francesco, R., Lior, R., and Bracha, S., “Introduction to recommender system” Recommender system handbook”. Springer-verlag, pp. 1-35, 2010.
- Paul, R., Neophytos, I., Mitesh, S., Peter, B., and John, R., “GroupLens: an open architecture for collaborative filtering of netnews,” ACM Conference on Computer Supported Cooperative Work, pp. 175–186, 1994.
- Walter, C., N., Maria L., Hernandez, A., Rafael, V., G., and Francisco, G., S., “Social knowledge-based recommender system. Application to the movies domain”. Expert Systems with applications, Elsevier, 39(12), pp 10990-11000, 2012.
- Mohammad, H., Nadimi, S., and Mozhde, B., Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique' Journal of Computing and Information Technology, Vol 3, Number 16, pp. 105-113, 2014.
- Miha, G., Dunja, M., Blaz, F., Marko, G., “Data Sparsity Issues in the Collaborative Filtering Framework” 7th International Workshop on Knowledge Discovery on the Web, Springer Berlin Heidelberg, pp 58-76, 2005.
- Billsus, M., and Pazzani, M., “Learning collaborative information filters”.in proceedings of the 15th international conferences on machine learning, Morgan Kaufmann Publishers, ACM, pp. 46-54, 1998.
- Poirier, D., Fessant, F., and Tellier, I., “Reducing the cold-start problem in content recommendation through opinion classification”. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 204-207, 2010.
- Qing, l., Byeong, M., K., “Clustering approach for hybrid recommender system”. International Conference on Web Intelligence, IEEE/WIC, pp. 33–38, 2003.
- Simon, P. and Shola, P., B., “A Paper Recommender System Based on the Past Ratings of a User” International Journal of Advanced Computer Technology, Vol 3, Issue 6, pp. 41-46, 2014.
- Khushboo, T., “Mining of Sentence Level Opinion Using Supervised Term Weighted Approach of Naïve Bayesian Algorithm”. International Journal of Computer Technology and Applications, 3(3) pp. 987-991, 2012.
- Robin, B., “Knowledge-based Recommender Systems”. Encyclopaedia of Library and Information Science, Marcel Dekker, pp. 180-200, 1992.
Back