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International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 3, March 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Precedent Behavioral Extraction System For Personalization Recommendation

[Full Text]

 

AUTHOR(S)

Mahima

 

KEYWORDS

Recommendation, Machine Learning, Personalization Behavior.

 

ABSTRACT

Hosting a compilation of billions of videos, YouTube presents one of the leading scale and most precious videos personalization recommendation system in existence. The recommendation system works on to personalized set of videos to users based on their past actions on the website. In this paper, we highlight the some of the major challenges that the system faces and how to address them. To tackle these issues, we have proposed a Precedent Behavioral Extraction Module (PBEM), which also deals with large-scale heterogeneous information to fulfill the requirements of the potential users. PBEM approach especially focus on the remarkable performance enhancements brought by machine learning. PBEM is a new approach as it works on discovering the precise web browsing behavior from uncertain keywords and defines the semantic measurement with user recommendation of keywords within the user query

 

REFERENCES

[1] Mukamakuza Carine, Sacharidis D, Werthner H, Mining User Behavior in Social Recommender Systems, ACM, pp 11-15, 2018.
[2] Gorgoglionea Michele, Pannielloa U, Tuzhilinb Alexander, Recommendation strategies in personalization applications, Information and Managemnet-Elsevier, pp 1-12, 2019.
[3] Chun-Hua Tsai, Peter Brusilovsky, Explaining Recommendations in an Interactive Hybrid Social Recommender, ACM, pp 391-396, 2019.
[4] Jiahui Liu, Peter Dolan, Elin Rønby Pedersen, Personalized News Recommendation Based on Click Behavior, ACM, pp 1-4. 2019.
[5] Paul Covington, Jay Adams, Emre Sargin, Deep Neural Networks for YouTube Recommendations, ACM, pp 9-13, 2016.
[6] Huan Yan, Chunfeng Yang, Donghan Yu, Yong Li, Depeng Jin, Dah Ming Chiu, Multi-site User Behavior Modeling and Its Application in Video Recommendation, JOURNAL OF IEEE TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING, pp 1-14, 2019.
[7] Sunny Thukral, Rana V, Versatility of Fuzzy Logic in Chronic Diseases: A Review, Journal of Medical Hypotheses, Elsevier, Vol 122, pp 150-156, 2018.
[8] Vijay Rana, Singh G, “An Analysis of Semantic Heterogeneity Issues and their Countermeasures Prevailing in Semantic Web”, ICROIT 2014, IEEE Xplore, pp 16-22, 2014.
[9] Sunny Sharma, Sunita, Vijay Rana, A semantic framework for ecommerce search engine optimization, International Journal of Information Technology-Springer, pp 1-6, 2018.
[10] Siddharth Patwardhan, Satanjeev Banerjee and Ted Pedersen, SenseRelate::TargetWord - A Generalized Framework forWord Sense Disambiguation, Association fo Computational Linguistics-ACM, pp 73-76, 2005.
[11] Simon Scheider and Werner Kuhn, How to Talk to Each Other via Computers: Semantic Interoperability as Conceptual Imitation, Applications of Conceptual Spaces Volume 359 of the series Synthese Library, pp 97-122, 2015.
[12] Gorgoglione Michele, Panniello, Tuzhilin, Recommendation strategies in personalization applications, Information & Management, pp 12-18, 2019.
[13] Shanahan T, Trang, Erik, Getting to know you: Social media personalization as a means of enhancing brand loyalty and perceived quality, Journal of Retailing and Consumer Services Vol 47, pp 57-65, 2019.
[14] Resnik, P, Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007, 1995.
[15] Sunita., & Rana, V, An Effective Preprocessing Algorithm for Information Retrieval System, International Journal of Recent Technology and Engineering (IJRTE). 8(3),pp 6371-6375, 2019.