Interactive User Intent Modeling

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Kangasrääsiö , Antti
dc.contributor.author Bhattarai, Shishir
dc.date.accessioned 2016-12-22T11:05:18Z
dc.date.available 2016-12-22T11:05:18Z
dc.date.issued 2016-12-12
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23901
dc.description.abstract In information retrieval systems, users often have difficulties in forming precise queries to express their information need. One approach to express information need is to explore the information space by providing relevance feedback to recommended items. This feedback is then used to model user search intent. Studies have shown how retrieval performance could be improved by allowing users to give feedback to multiple items such as keywords and documents instead of keywords only. In this thesis, I extend an existing user model which uses document-level and keyword-level feedback to include session-level feedback, and study the usefulness of this extension. By conducting simulation studies in various settings, I investigate the effect of session-level feedback. Based on these simulation results, I conclude that additional session-feedback helps in finding relevant documents by improving F1-score. Results show that more the additional session-feedback, more the improvement in F1-score. However, trade-off of session-feedback instead of document and keyword feedback results in drop in document F1-score, therefore indicating that session-feedback is less informative than document and keyword feedback. en
dc.format.extent 44
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Interactive User Intent Modeling en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword information re-finding en
dc.subject.keyword information retrieval en
dc.subject.keyword interactive user intent modeling en
dc.subject.keyword multi-armed bandit problem en
dc.identifier.urn URN:NBN:fi:aalto-201612226194
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3044 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Kaski, Samuel
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi


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