Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Context-aware search personalization with concept preference
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Most of major search engines develop different types of personalisation of search results. Personalisation includes deriving user's long-term preferences, query disambiguation etc. User sessions provide very powerful tool commonly used for these problems. In this paper we focus on personalisation based on context-aware reranking. We implement a machine learning framework to approach this problem and study importance of different types of features. We stress that features concerning temporal and context relatedness of queries along with features relied on user's actions are most important and play crucial role for this type of personalisation.