Ranking model construction is an important topic in information retrieval. Recently, many approaches based on the idea of ¡§learning to rank¡¨ have been proposed for this task and most of them attempt to score all documents of different queries by resorting to a single function. In this thesis, we propose a novel framework of query-dependent ranking. A simple similarity measure is used to calculate similarities between queries. An individual ranking model is constructed for each training query with corresponding documents. When a new query is asked, documents retrieved for the new query are ranked according to the scores determined by a ranking model which is combined from the models of similar training queries. A mechanism for determining combining weights is also provided. Experimental results show that this query dependent ranking approach is more effective than other approaches.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0828109-151321 |
| Date | 28 August 2009 |
| Creators | Lee, Lian-Wang |
| Contributors | Jeng-Shyang Pan, Shing-Tai Pan, Chang-Shing Lee, Shie-Jue Lee, Been-Chian Chien |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828109-151321 |
| Rights | not_available, Copyright information available at source archive |
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