Sentiment Digitization Modeling for Recommendation System
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ikram Karabila & Nossayba Darraz & Anas El-Ansari & Nabil Alami & Mostafa El Mallahi, 2023. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis," Future Internet, MDPI, vol. 15(7), pages 1-21, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
- Li, Man & Wen, Luosheng & Chen, Feiyu, 2021. "A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
- Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
- Jun Yao & Jianhui Chen, 2023. "A Study on the Characteristics of Middle-aged Chinese Female Users Based on Clothing Needs," Asian Social Science, Canadian Center of Science and Education, vol. 19(4), pages 1-86, August.
- Yong Eui Kim & Sang-Min Choi & Dongwoo Lee & Yeong Geon Seo & Suwon Lee, 2023. "A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
- Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.
- Liang Xiao & Qibei Lu & Feipeng Guo, 2020. "Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
- Sang-Min Choi & Dongwoo Lee & Kiyoung Jang & Chihyun Park & Suwon Lee, 2023. "Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
More about this item
Keywords
sentiment analysis; sentiment dictionary; recommendation system; text mining; collaborative filtering;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:5191-:d:376262. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.