Automatically learning a ranking model is becoming an essential task for effective information retrieval. Its advantage lies in the ability to combine the user feedback with the conceptual features. However, learning to rank methods require large, training and test data sets, to gather feedback from the user. Existing learning-to-rank methods focus either on user feedback or at the document level or on query-dependent feature scores. In this paper, we consider the implicit and explicit feedback from the user using a set of test data. This test data is given as input to the training phase, in which we find the document conceptual and query-dependent document features. Hence, the impact of the user feedback on the search query is identified by learning the document, and query level conceptual features, using statistical methods. A DCG (Discounted Cumulative gain) score is calculated and used in comparing the ranking in subsequent iterations. The learning process continues until there is no change in the DCG score. The learned ranking model is compared with our base ranking method and achieved 20% improvements in nDCG score.
['E. Umamaheswari', 'T. V. Geetha']
Learning a Concept Based Ranking Model with User Feedback