Abstract
Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated with the conversations. We compared the effects of spoken conversational input in four conditions: a control condition without contextualization; an experimental condition with the model using search query logs; an experimental condition with the model using spoken conversational input; and an experimental condition with the model using both search query logs and spoken conversational input. We show the advantage of combining the spoken conversational context with the Web-search context for improved retrieval performance. Our results suggest that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 31 |
| Tidsskrift | ACM Transactions on Information Systems |
| Vol/bind | 39 |
| Udgave nummer | 3 |
| ISSN | 1046-8188 |
| DOI | |
| Status | Udgivet - 2021 |
Bibliografisk note
Funding Information:This research was funded by the project COADAPT (Human and Work Station Adaptation Support to aging citizens, Grant Agreement No. 826266) and the project PON AIM (ID No. AIM1875400-1, CUP No. B74I18000210006), and was partially supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI and decision numbers: 322653, 328875, 336085). Authors’ addresses: T. Vuong and G. Jacucci, University of Helsinki, Pietari Kalmin katu 5 00560 Helsinki, Finland; emails: [email protected], [email protected]; S. Andolina, University of Palermo, Via Archirafi 34 90123 Palermo, Italy and University of Helsinki, Pietari Kalmin katu 5 00560 Helsinki, Finland; email: [email protected]; T. Ruotsalo, University of Helsinki, Pietari Kalmin katu 5 00560 Helsinki, Finland, University of Copenhagen, Universitetsparken 1 2100 Copenhagen, Denmark; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 1046-8188/2021/05-ART31 $15.00 https://doi.org/10.1145/3447875
Publisher Copyright:
© 2021 Association for Computing Machinery.