Review And Ranking Model In Search Engines Using Unsupervised Techniques

Review and Ranking Model in Search Engines Using Unsupervised Techniques

Abstract:-

Search-engine logs provide a wealth of information that machine-learning techniques can connect to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection. Each time a user formulates a query or clicks on a search result; easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine’s natural use, and is specific to a particular user and collection. A smart search engine could use this implicit feedback to learn personalized ranking functions.

In this paper, we address these difficulties by proposing a regularization-based on machine learning algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labelled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimation. Search engine demonstrates the applicability’s of the proposed ranking adaptation algorithms and the ranking adaptability measurement.