Recommendations of Products Based on Combination of Collaborative, Content and Pearson Filtering

Recommendations of Products Based on Combination of Collaborative, Content and Pearson Filtering

Jyostna D. Bodapati Naralasetti VeeranjaneyuluMalkari M. Rao

Vignan’s Foundation for Science, Technology and Research, Vadlamudi 522213, AP, India

Corresponding Author Email: 
veeru2006n@gmail.com
Page: 
70-75
|
DOI: 
https://doi.org/10.18280/ama_b.610203
Received: 
18 April 2018
|
Accepted: 
31 May 2018
|
Published: 
30 June 2018
| Citation

OPEN ACCESS

Abstract: 

In today’s world the numbers of ecommerce companies are increasing day by day and also huge number of products is coming into the market. When customers want to buy the products they generally just see a numerical rating of the products and then purchase them. Later they come to know that products are not good.  3 kinds of rating systems are implemented in this work namely collaborative rating, content based rating and Pearson rating.  In the implementation makes use of latest technology stack namely spring framework for the backend and Ext JS Framework for the front end. Content Based recommendations are based on user past transactions, Collaborative recommendations are based on rating from across the users and Pearson recommendations takes logged in user and other user ratings to provide better quantifying recommendations.

Keywords: 

content, collaborative and pearson recommendations

1. Introduction
2. Back Ground
3. Algorithms
4. Implementation Details
5. Results
6. Conclusions
  References

[1] Praveena M, Bincy K, Vinayak H. (2016). Book recommendation system through content based and collaborative filtering method. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE).

[2] Yee W.Lo; Vidyasagar P. (2009). A review of opinion mining and sentiment  classification  framework in social networks. 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[3] L'Huillier G, Hevia A, Weber R, Ríos S, (2010). Latent semantic analysis and keyword extraction for phishing classification. 2010 IEEE International Conference on Intelligence and Security Informatics.

[4] Sachan A, Richariya V. (2013). Survey on Recommender System based on Collaborative Technique. Department of Computer Science And Engineering intemational journal of innovations in engineering and technology(IJIET) 2(2): 1-7.

[5] Tewari AS, Kumar A, Barman AG. (2014). Book recommendation system based on combine features of content based filtering and association rule mining. IEEE International I Advance Computing Conference(IACC), pp. 502.

[6] Sie SH, Yeh JH. (2014). Library book recommendations based on latent topic aggregation. International Publishing Switzerland, 411-416.

[7] Pranav B, Navinkumar A. (2015). Book recommendation system using opinion mining technique. International Journal of Research in Engineering andTechnology(IJRET) 4(1): 333.

[8] Sun D, Luo Z, Zhang F. (2011). A novel approach for collaborative filtering to alleviate the new item cold start problem. IEEE 11th International Symposium on Communications and Information Technologies (ISCIT) 402-406. 

[9] Xie F, Xu M, Chen Z. (2012). RBRA: A simple and efficient rating-based recommender algorithm to cope with sparsity in recommender systems. IEEE 26th International Conference on Advanced Information Networking and Applications Workshops. 

[10] Chikhaoui B, Chiazzaro M, Wang S. (2011). An Improved Hybrid Recommender System By Combining Predictions. IEEE Workshops of International Conference on Advanced Information Networking and Applications, pp. 644-649. 

[11] Deng XY, Fei F, Huang F. (2017). An effective recommendation model using user access sequence and context entropy. AMSE Journals, AMSE IIETA Publication, Series: Advances B 60(1): 57-73.

[12] Moghaddam SG, Selamat A. (2011). A scalable collaborative recommender algorithm based on user density-based clustering. 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), pp. 246-249.