@inproceedings{davide2021costeffective, title = {Cost-effective Identification of On-topic Search Queries using Multi-Armed Bandits}, booktitle = {ACM Symposium on Applied Computing}, year = {2021}, abstract = {Identifying 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.}, doi = {10.1145/3412841.3441944}, url = {http://dx.doi.org/10.1145/3412841.3441944}, author = {David E. Losada‚ Matthias Herrmann‚ David Elsweiler} }