Ranking Algorithm RA-SVM for Domain Adaptation: A Review
Paras V. Kothare, Yogesh K. Gedam, Ratnadeep R. Deshmukh · 2014
Different new vertical domains are coming everyday so running a broad-based ranking model is no longer desirable as the domain are different and building a separate model for each domain is also not beneficial because there much time required for labeling the data and training the samples. In this paper we are handling the above problem by regularization based algorithm called as ranking adaptation SVM (RA-SVM), the algorithm is used to adapt existing ranking model of broad-based search engine to new domain. Here performance is still guaranteed and times taken to label the data training the samples are reduced. The algorithms only requires prediction from existing ranking model and do not require internal structure of it. Adapted ranking model concentrate on specific domain to achieve better results which are relevant to the search, further it reduces the searching cost also as the most appropriate search results are shown. Single ranking model is not good for training the search engine as the information retrieval is complicated, Domains are highly great, used in the global search engines and the data set is also large. So we can't generalize the information well for specific search intensions. That is why we are moving towards training the global ranking model for each specific domain for fetching the appropriate information from each respected domain for doing so we are using robust supervised classification algorithm. The parameters learned during the model adaptation and ranking SVM from global ranking model are capable retrieving the required information. Adapting the model is lot easier than building a unique ranking model for each domain.