An adaptive learning to rank algorithm: Learning automata approach

The recent years have witnessed the birth and explosive growth of the web. It is obvious that the exponential growth of the web has made it into a huge interconnected source of information wherein finding a document without a searching tool is unimaginable. Today's search engines try to provide the most relevant suggestions to the user queries. To do this, different strategies are used to enhance the precision of the information retrieval process. In this paper, a learning method is proposed to rank the web documents in a search engine. The proposed method takes advantage of the user feedback to enhance the precision of the search results. To do so, it uses a learning automata‐based approach to train the search engine. In this method, the user feedback is defined as its interest to review an item. Within the search results, the document that is visited by the user is more likely relevant to the user query. Therefore, its choice probability must be increased by the learning automaton. By this, the rank of the most relevant documents increases as that of the others decreases. To investigate the efficiency of the proposed method, extensive simulation experiment is conducted on well‐known data collections. The obtained results show the superiority of the proposed approach over the existing methods in terms of mean average precision, precision at position n, and normalized discount cumulative gain.