Name
Abstract | ||
|---|---|---|
For improving the effectiveness of Interactive Information Retrieval (IIR), a system should minimise the search time by guiding the user appropriately. As a prerequisite, in any search situation, the system must be able to estimate the time the user will need for finding the next relevant document. In this paper, we show how Markov models derived from search logs can be used for predicting search times, and describe a method for evaluating these predictions. For personalising the predictions based upon a few user events observed, we devise appropriate parameter estimation methods. Our experimental results show that by observing users for only 100 seconds, the personalised predictions are already significantly better than global predictions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3121050.3121085 | ICTIR'17: PROCEEDINGS OF THE 2017 ACM SIGIR INTERNATIONAL CONFERENCE THEORY OF INFORMATION RETRIEVAL |
Field | DocType | Citations |
Information system,Data mining,Markov model,Computer science,Infinite impulse response,Markov chain,User modeling,Estimation theory | Conference | 3 |
PageRank | References | Authors |
0.38 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vu Tran | 1 | 18 | 1.09 |
| David Maxwell | 2 | 65 | 7.39 |
| Norbert Fuhr | 3 | 3496 | 939.53 |
| Leif Azzopardi | 4 | 1919 | 133.10 |