Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Content-based filtering and personalization using structured metadata
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Knowledge and Data Engineering
WMR--A Graph-Based Algorithm for Friend Recommendation
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
Harvesting with SONAR: the value of aggregating social network information
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Public vs. private: comparing public social network information with email
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A multi-disciplinar recommender system to advice research resources in University Digital Libraries
Expert Systems with Applications: An International Journal
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Linking social networks on the web with FOAF: a semantic web case study
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Graph-Based Recommendation on Social Networks
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Minimum weight covering problems in stochastic environments
Information Sciences: an International Journal
Revealing network communities with a nonlinear programming method
Information Sciences: an International Journal
People-to-People recommendation using multiple compatible subgroups
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
| Hi-index | 0.07 |
As the number of Twitter users exceeds 175 million and the scale of social network increases, it is facing with a challenge to how to help people find right people and information conveniently. For this purpose, current social network services are adopting personalized recommender systems. Existing recommendation algorithms largely depend on one of content-based algorithm, collaborative filtering, or influential ranking analysis. However, these algorithms tend to suffer from the performance fluctuation phenomenon in common whenever an active user changes, and it is due to the diversities of personal characteristics such as the local social graph size, the number of followers, or sparsity of profile content. To overcome this limitation and to provide consistent and stable recommendation in social networks, this study proposes the dynamic competitive recommendation algorithm based on the competition of multiple component algorithms. This study shows that it outperforms previous approaches through performance evaluation on actual Twitter dataset.