Using Collaborative Models to Adaptively Predict Visitor Locations in Museums
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting professional versus personal closeness using an enterprise social network site
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social networking feeds: recommending items of interest
Proceedings of the fourth ACM conference on Recommender systems
Identifying relevant social media content: leveraging information diversity and user cognition
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Selecting items of relevance in social network feeds
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Personalized techniques for lifestyle change
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Personalized activity streams: sifting through the "river of news"
Proceedings of the fifth ACM conference on Recommender systems
Network activity feed: finding needles in a haystack
Proceedings of the 4th International Workshop on Modeling Social Media
| Hi-index | 0.00 |
Social networking systems originally emerged as tools for keeping up with the daily lives of friends and strangers. They have established themselves as valuable resources and means to satisfy information needs. The challenge with information seeking through social networks is that their immense success and popularity is also a weakness. The data deluge facing users has surpassed comfortably managed levels and can impact on the quality and relevance of the information consumed. We developed a personalized model for predicting the relevance of news feed items, in order to facilitate personalized feeds. Results of a live analysis show that our approach successfully identifies and promotes relevant feed items, with the knock-on effects of increasing interaction between users and the contribution of user generated content.