Machine Learning
User and task analysis for interface design
User and task analysis for interface design
Machine Learning - Special issue on learning with probabilistic representations
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive interfaces and agents
The human-computer interaction handbook
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 11th international conference on Intelligent user interfaces
User-centered evaluation of adaptive and adaptable systems: A literature review
The Knowledge Engineering Review
Linked internet UI: a mobile user interface optimized for social networking
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Social networking feeds: recommending items of interest
Proceedings of the fourth ACM conference on Recommender systems
Speak little and well: recommending conversations in online social streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A novel mobile device user interface with integrated social networking services
International Journal of Human-Computer Studies
| Hi-index | 0.00 |
Many people follow social networking services and find it difficult to locate essential content on mobile devices. Automatic filtering of the feeds is one solution to this problem. A system learns a model for each user, based on metadata (e.g., content types and contacts) and click histories for old feed items, predicts the click probability for incoming items, and automatically filters out less important ones. In this study, we implemented several alternative automatic filtering systems and evaluate their offline accuracy and user acceptance. 40 users completed the evaluation in a field study. Two main findings emerge from the study. Firstly, PageRank and Bayesian predictors are valid methods; an ensemble predictor combining the two further improves the prediction accuracy. Secondly, people show high acceptance of the automatic filtering function. The participants using the filtering function found it easier to access interesting content than did the participants without the filtering. On average, they also felt greater sense of control, due to the reduced feed volume.