Next Article in Journal
No More Privacy Any More?
Previous Article in Journal
An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(1), 18; https://doi.org/10.3390/info10010018

A Recommendation Model Based on Multi-Emotion Similarity in the Social Networks

1
School of Information Science and Engineering, Central South University, Changsha 410083, China
2
School of Computer, Hunan University of Technology, Zhuzhou 412000, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
(This article belongs to the Special Issue Social Networks and Recommender Systems)
Full-Text   |   PDF [2918 KB, uploaded 6 January 2019]   |  
  |   Review Reports

Abstract

This paper proposed a recommendation model called RM-SA, which is based on multi-emotional analysis in networks. In the RM-MES scheme, the recommendation values of goods are primarily derived from the probabilities calculated by a similar existing recommendation system during the initiation stage of the recommendation system. First, the behaviors of those users can be divided into three aspects, including browsing goods, buying goods only, and purchasing–evaluating goods. Then, the characteristics of goods and the emotional information of user are considered to determine similarities between users and stores. We chose the most similar shop as the reference existing shop in the experiment. Then, the recommendation probability matrix of both the existing store and the new store is computed based on the similarities between users and target user, who are randomly selected. Finally, we used co-purchasing metadata from Amazon and a certain kind of comments to verify the effectiveness and performance of the RM-MES scheme proposed in this paper through comprehensive experiments. The final results showed that the precision, recall, and F1-measure were increased by 19.07%, 20.73% and 21.02% respectively. View Full-Text
Keywords: recommendation model; social network; multi-emotion; cold start recommendation model; social network; multi-emotion; cold start
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Long, J.; Wang, Y.; Yuan, X.; Li, T.; Liu, Q. A Recommendation Model Based on Multi-Emotion Similarity in the Social Networks. Information 2019, 10, 18.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top