User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

By Zhi-Dan Zhao, Mingsheng Shang. 2010 Third International Conference on Knowledge Discovery and Data Mining, 2010

437 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/5d06f630188a5ec9c05c4961eddbf9f24e2e6916

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Qiang Yang
2. Yu Zhang

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Yu Zhang
3. Guangneng Hu
4. Yetunde Folajimi
5. Bolanle Adefowoke Ojokoh
6. Folasade Olubusola Isinkaye
7. Gabriella Csurka
8. P. Li

In Information Systems

1. Qiang Yang
2. Guangneng Hu
3. Yu Zhang
4. Yetunde Folajimi
5. Bolanle Adefowoke Ojokoh
6. Folasade Olubusola Isinkaye
7. Weike Pan
8. Sotirios P. Chatzis

Citations in the corpus

• Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (2017, neutral): “CF-based Methods: • UCF [46].”
• Recommendation systems: Principles, methods and evaluation (2015, background): “The items that were already rated by the user before play a relevant role in searching for a neighbor that shares appreciation with him [45,46].”
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018, background): “Collaborative filtering (CF) alleviates this issue by predicting user preferences based on the user-item interaction behavior, agnostic to the content [7,46].”