Name
Abstract | ||
|---|---|---|
ABSTRACTIdentifying the topic of a search query is a challenging problem, for which a solution would be valuable in diverse situations. In this work, we formulate the problem as a ranking task where various rankers order queries in terms of likelihood of being related to a specific topic of interest. In doing so, an explore-exploit trade-off is established whereby exploiting effective rankers may result in more on-topic queries being discovered, but exploring weaker rankers might also offer value for the overall judgement process. We show empirically that multi-armed bandit algorithms can utilise signals from divergent query rankers, resulting in improved performance in extracting on-topic queries. In particular we find Bayesian non-stationary approaches to offer high utility. We explain why the results offer promise for several use-cases both within the field of information retrieval and for data-driven science, generally. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3412841.3441944 | Symposium on Applied Computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David E. Losada | 1 | 326 | 40.63 |
| Matthias Herrmann | 2 | 0 | 0.34 |
| David Elsweiler | 3 | 416 | 40.65 |