A Relevance Evaluation Model of Information Retrieval Based on Weighted Term Frequency

Relevance evaluation model is an important research issue in the field of information retrieval.The bas- ic information retrieval models are boolean model,vector space model and probabilistic model.The latter two models are implemented in many retrieval systems extensively but the different position of query term in every document is ig- nored.Some researches have considered the information HTML tags but the scheme of assigning weighted parameters is not ideal.In this paper,WTFM(Weighted Term Frequency Model)is proposed according to the existence of term frequency(TF).And these weighted coefficients are learned by simulated annealing algorithm.The results of the ex- periments show that the introduction of TF's weights brings improvements to the system.