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|Title:||Personalized information retrieval based on novelty feeback||Authors:||YIN HAINAN||Keywords:||Personalized information retrieval, relevance judgment, topicality, novelty, relevance decision rule, incomplete learning||Issue Date:||21-Jul-2005||Citation:||YIN HAINAN (2005-07-21). Personalized information retrieval based on novelty feeback. ScholarBank@NUS Repository.||Abstract:||The existing techniques of personalized information retrieval (PIR) mainly focus on topicality and ignore another important dimension of relevance judgment: novelty. Building on past user studies, this thesis proposes a novelty-based approach to PIR which incorporates both topicality and novelty as relevance criteria. More specifically, we propose four propositions regarding topicality and novelty in relevance judgment. We hypothesize that (1) novelty judgment is a value-added criterion to improve PIR, (2) relevance measures in past system-centered PIR studies are biased toward topicality, (3) usera??s novelty judgment standard is directed toward a subtopic and is slowly changing because usera??s learning of document content in retrieval process is incomplete, and (4) relevance judgment of a document starts with topicality judgment followed by novelty judgment in a stepwise fashion. A set of PIR systems are designed to implement these propositions. Our user test supports these propositions except for last one which might be insignificant because of the specific nature of the testing corpus.||URI:||http://scholarbank.nus.edu.sg/handle/10635/14774|
|Appears in Collections:||Master's Theses (Open)|
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