مدل جدیدی برای جستجوی عبارت بر اساس کمینه جابه‌جایی وزن‌دار

Finding high-quality web pages is one of the most important tasks of search engines. The relevance between the documents found and the query searched depends on the user observation and increases the complexity of ranking algorithms. The other issue is that users often explore just the first 10 to 20 results while millions of pages related to a query may exist. So search engines have to use suitable algorithms with high performance to find the most relevant pages. The ranking section is an important part of search engines. Ranking is a process in which the web page quality is estimated by the search engine. There are two main methods for ranking web pages. In the first method, ranking is done based on the documents’ content (traditional rankings). Models, such as Boolean model, probability model and vector space model are used to rank documents based on their contents. In the second method, based on the graph, web connections and the importance of web pages, ranking process is performed. Based on researches on search engines, the majority of user queries is more than one term. For queries with more than one term, two models can be used. The first model assumes that query terms are independent of each other while the second model considers a location and order dependency between query terms. Experiments show that in the majority of queries there are dependencies between terms. One of the parameters that can specify dependencies between query terms is the distance between query terms in the document. In this paper, a new definition of distance based on Minimum Weighted Displacement Model (MWDM) of document terms to accommodate the query terms is presented. In the Minimum Weighted Displacement Model (MWDM), we call the minimum number of words moving a text to match the query term by space. In addition, because most of the ranking algorithms use the TF (Term Frequency) to score documents and for queries more than one term, there is no clear definition of these parameters; in this paper, according to the new distance concept, Phrase Frequency and Inverted Document Frequency are defined. Also, algorithms to calculate them are presented. The results of the proposed algorithm compared with multiple corresponding algorithms shows a favorable increase in average precision.