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.
| Original language | English |
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| Title of host publication | WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining |
| Number of pages | 1 |
| Publisher | Association for Computing Machinery, Inc. |
| Publication date | 2 Feb 2017 |
| ISBN (Electronic) | 9781450346757 |
| DOIs | |
| Publication status | Published - 2 Feb 2017 |
| Externally published | Yes |
| Event | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom Duration: 6 Feb 2017 → 10 Feb 2017 |
Conference
| Conference | 10th ACM International Conference on Web Search and Data Mining, WSDM 2017 |
|---|---|
| Country/Territory | United Kingdom |
| City | Cambridge |
| Period | 06/02/2017 → 10/02/2017 |
| Sponsor | ACM SIGKDD, ACM SIGMOD, ACM SIGWEB, Special Interest Group on Information Retrieval (ACM SIGIR) |
Keywords
- Evaluation
- Markov precision
- User model