Abstract
User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.
Original language | English |
---|---|
Title of host publication | Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 210-213 |
Number of pages | 4 |
ISBN (Electronic) | 9781509012459 |
DOIs | |
State | Published - 10 Jul 2017 |
Event | 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 - Beijing, China Duration: 23 Sep 2016 → 25 Sep 2016 |
Publication series
Name | Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 |
---|
Conference
Conference | 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 |
---|---|
Country/Territory | China |
City | Beijing |
Period | 23/09/16 → 25/09/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Collaborative filtering
- Efficiency
- Multidimensional indexing