Learning to rank web data using multivariate adaptive regression splines

Altınok, Gülşah
A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches such as Boolean Model (BM), Vector Space Model (VSM) and classical Probabilistic Model (classical PM). The second approach is called Language Model (LM). Such models are n-gram Model, Query Likelihood Model (QLM). The final method is namely system model including Support Vector Model (SVM) and Artificial Neural Network (ANN). In this study, we adopted the system model approach and compared the performance measures of those widely used models, SVM and ANN with those Multivariate Adaptive Regression Splines (MARS) and its variant Conic Multivariate Adaptive Regression Splines (CMARS). Results indicate that MARS performs slightly better than the others considered in this study


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Data Mining is becoming a famous analysis day by day to reveal the hidden information within big data. In the study, we use data mining techniques on the economic indicators of the countries. The four data mining techniques are to be implemented on the dataset. Making homogenous groups of the countries whose economic characteristics are similar are obtained by the Clustering Algorithm. After the clustering algorithm is performed, we pass to Association Rule Data Mining to investigate the most exported produ...
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Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance
Koc, Elcin Kartal; İyigün, Cem; Batmaz, İnci; Weber, Gerhard-Wilhelm (2014-09-01)
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The huge amount of information existing in the World Wide Web constitutes an ideal environment to implement data mining techniques. Web mining is the mining of web data. There are different applications of web mining: web content mining, web structure mining and web usage mining. In our study we analyzed an online course by web usage mining techniques in order to optimize the navigation paths, the duration of the time spend on each page and the number of visits throughout the semester of the course. Moreove...
Citation Formats
G. Altınok, “Learning to rank web data using multivariate adaptive regression splines,” M.S. - Master of Science, Middle East Technical University, 2018.