Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hybrid web recommender systems
The adaptive web
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
New objective functions for social collaborative filtering
Proceedings of the 21st international conference on World Wide Web
| Hi-index | 0.00 |
This paper describes recommendation techniques that help users to find potentially interesting people to follow at Twitter. The explored techniques are based on a confirmed assumption that the recent activity of users is indicative of their latest friend preferences. Several content-based recommendation strategies are explored, compared and tested. Among them the foundations for a novel hybridization framework are provided and a multi-view approach towards modeling user profiles is considered. The training and test database is crawled with real users and tweets from the Twitter network. A non-standard evaluation scheme is applied in an offline testing context for the various algorithms. Conclusions are drawn as to the viability, relative predictive power and accuracy of the recommendation approaches.