TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Conversational tagging in twitter
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Semantic enrichment of twitter posts for user profile construction on the social web
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Interweaving Trend and User Modeling for Personalized News Recommendation
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Hi-index | 0.00 |
Social Web describes a new culture of participation on the Web where more and more people actively participate in publishing and organizing Web content. As part of this culture, people leave a variety of traces when interacting with (other people via) Social Web systems. In this paper, we investigate user modeling strategies for inferring personal interest profiles from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles change over time. Moreover, we evaluate the quality of the user modeling strategies in the context of personalized recommender systems and show that those strategies which consider the temporal dynamics of the individual profiles allow for the best performance.