Adapting a Ranking Model for Domain-Specific Search
|Name||Adapting a Ranking Model for Domain-Specific Search|
An adaptation process is described to adapt a ranking model constructed for a broad-based search engine for use with a domain-specific ranking model. It’s difficult to applying the broad-based ranking model directly to different domains due to domain differences, to build a unique ranking model for each domain it time-consuming for training models. In this paper,we address these difficulties by proposing algorithm called ranking adaptation SVM (RA-SVM), Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains The ranking model is adapted for use in a search environment focusing on a specific segment of online content, for example, a specific topic, media type, or genre of content. a domain-specific ranking model reduces search results to the data from a specific domain that are relevant with respect to the search terms input by the user. The ranking order may be determined with reference to a given numerical score, an ordinal score, or a binary judgment such as “relevant” or “irrelevant”.
|ieee paper year||2013|