CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

An Approach to Increase Efficiency of Opinion Mining and Recommendation Collaborative Filtering System using Similarity-based Algorithms

Credit to Download: 1 | Page Numbers 11 | Abstract Views: 89
Year: 2018
COI code: TECCONF04_048
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors An Approach to Increase Efficiency of Opinion Mining and Recommendation Collaborative Filtering System using Similarity-based Algorithms

  seyyed hossein hosseini - Department of Computer, Faculty of Engineering, Islamic Azad University, Damavand Branch,Damavand, Iran
  Javad Hosseinkhani - Department of Computer Engineering/ Islamic Azad University, Zahedan Branch, Iran Zahedan, Iran

Abstract:

Increasingly increasing social networking has led many users to take that part and often manage their daily activities on these networks. The traditional similarity criteria, such as the Pearson correlation coefficient (PCC, cosine similarity (COS), and mean square difference (MSD), assume similarity of a symmetric concept, that is, two users have the same effect on each other.) A subtle point that is worrying is ignoring the criteria the similarities between the different parts of the items are shared between the two users. Equivalence functions produce the equivalence ratio for two users because they penalize both users for different parts of the shared items. Recommender systems are one of the most successful web-based personalization tools. The most important task of an advisory system is to discover the user s favorite items. Extremely large spaces are selectable items. Similarity algorithms are often considered as collaborative refinement techniques based on memory and one of the most successful methods in advisory systems. When explicit the similarity is usually defined using similarity functions, such as Pearson correlation coefficient, cosine similarity, or mean square difference. These criteria assume that the similarity is symmetric, so that the two users introduce new equivalence effects on Have one another in this thesis, we introduce new weighing schemes that allow us to consider new features to find similarities between users. These weigh-in schemes first convert the symmetric similarity to asymmetric similarity by considering the number of users votes to the items. Then, the impact of user habits on ranking items is considered by measuring the number of repetitions of each rank for the ranked items in common. Experiments will be performed on the data set and the results will be compared with similarity criteria. The results will show that the addition of weightedmethods to the traditional similarity criteria significantly improves the results of them.

Keywords:

Advisor system, Opinion mining system, Participatory filtering, Social networking, Similarity algorithms.

Perma Link

https://www.civilica.com/Paper-TECCONF04-TECCONF04_048.html
COI code: TECCONF04_048

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
hosseini, seyyed hossein & Javad Hosseinkhani, 2018, An Approach to Increase Efficiency of Opinion Mining and Recommendation Collaborative Filtering System using Similarity-based Algorithms, Forth National Conference on Electrical and Computer Engineering, تهران, دانشگاه پيام نور, https://www.civilica.com/Paper-TECCONF04-TECCONF04_048.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (hosseini, seyyed hossein & Javad Hosseinkhani, 2018)
Second and more: (hosseini & Hosseinkhani, 2018)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: Azad University
Paper No.: 1426
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.