Abstract
To address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjust it to user interactions and preferences we develop a new model of user behavior based onMarkov chains. We aim at integrating the proposed model into several aspects of IR, i.e. evaluation measures, systems and collections. Firstly, we studied IR evaluation measures and we propose a theoret- ical framework to describe their properties. Then, we pre- sented a new family of evaluation measures, called Markov Precision (MP), based on the proposed model and able to explicitly link lab-style and on-line evaluation metrics. Fu- ture work will include the presented model into Learning to Rank (LtR) algorithms and will define a collection for evaluation and comparison of Personalized Information Re- trieval (PIR) systems.
| Originalsprog | Engelsk |
|---|---|
| Titel | WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining |
| Antal sider | 1 |
| Forlag | Association for Computing Machinery, Inc. |
| Publikationsdato | 2 feb. 2017 |
| ISBN (Elektronisk) | 9781450346757 |
| DOI | |
| Status | Udgivet - 2 feb. 2017 |
| Udgivet eksternt | Ja |
| Begivenhed | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, Storbritannien Varighed: 6 feb. 2017 → 10 feb. 2017 |
Konference
| Konference | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 |
|---|---|
| Land/Område | Storbritannien |
| By | Cambridge |
| Periode | 06/02/2017 → 10/02/2017 |
| Sponsor | ACM SIGKDD, ACM SIGMOD, ACM SIGWEB, Special Interest Group on Information Retrieval (ACM SIGIR) |