Recommendation system to simplify opinion formation

R. Vignesh; Kumar Rishabh

doi:10.1109/ICDMAI.2017.8073475

Recommendation system to simplify opinion formation

R. Vignesh, Kumar Rishabh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper aims at introducing a new way of recommending movies to users. It is an improvement on the existing approaches of Content Based Recommendation system and Collaborative Filtering. Creating similar feature vectors for both the users and movies, we update it with every passing recommendation made. We then find out the nearest user by calculating the difference in the feature using root mean square error technique. We finally draw out a conclusion and observe the cases where this outperforms other popular algorithms. We also look at its shortcomings and list the scope for future improvements that could be made.

Original language	English
Title of host publication	2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781509040834
DOIs	https://doi.org/10.1109/ICDMAI.2017.8073475
Publication status	Published - 18-10-2017
Externally published	Yes
Event	1st International Conference on Data Management, Analytics and Innovation, ICDMAI 2017 - Pune, India Duration: 24-02-2017 → 26-02-2017

Conference

Conference	1st International Conference on Data Management, Analytics and Innovation, ICDMAI 2017
Country/Territory	India
City	Pune
Period	24-02-17 → 26-02-17

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Information Systems and Management

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10.1109/ICDMAI.2017.8073475

Cite this

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title = "Recommendation system to simplify opinion formation",

abstract = "This paper aims at introducing a new way of recommending movies to users. It is an improvement on the existing approaches of Content Based Recommendation system and Collaborative Filtering. Creating similar feature vectors for both the users and movies, we update it with every passing recommendation made. We then find out the nearest user by calculating the difference in the feature using root mean square error technique. We finally draw out a conclusion and observe the cases where this outperforms other popular algorithms. We also look at its shortcomings and list the scope for future improvements that could be made.",

author = "R. Vignesh and Kumar Rishabh",

year = "2017",

month = oct,

day = "18",

doi = "10.1109/ICDMAI.2017.8073475",

language = "English",

pages = "1--4",

booktitle = "2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "1st International Conference on Data Management, Analytics and Innovation, ICDMAI 2017 ; Conference date: 24-02-2017 Through 26-02-2017",

}

Vignesh, R & Rishabh, K 2017, Recommendation system to simplify opinion formation. in 2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017., 8073475, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 1st International Conference on Data Management, Analytics and Innovation, ICDMAI 2017, Pune, India, 24-02-17. https://doi.org/10.1109/ICDMAI.2017.8073475

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AU - Rishabh, Kumar

PY - 2017/10/18

Y1 - 2017/10/18

N2 - This paper aims at introducing a new way of recommending movies to users. It is an improvement on the existing approaches of Content Based Recommendation system and Collaborative Filtering. Creating similar feature vectors for both the users and movies, we update it with every passing recommendation made. We then find out the nearest user by calculating the difference in the feature using root mean square error technique. We finally draw out a conclusion and observe the cases where this outperforms other popular algorithms. We also look at its shortcomings and list the scope for future improvements that could be made.

AB - This paper aims at introducing a new way of recommending movies to users. It is an improvement on the existing approaches of Content Based Recommendation system and Collaborative Filtering. Creating similar feature vectors for both the users and movies, we update it with every passing recommendation made. We then find out the nearest user by calculating the difference in the feature using root mean square error technique. We finally draw out a conclusion and observe the cases where this outperforms other popular algorithms. We also look at its shortcomings and list the scope for future improvements that could be made.

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M3 - Conference contribution

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BT - 2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st International Conference on Data Management, Analytics and Innovation, ICDMAI 2017

Y2 - 24 February 2017 through 26 February 2017

ER -

Recommendation system to simplify opinion formation

Abstract

Conference

All Science Journal Classification (ASJC) codes

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