Nowadays, a large number of third-party services suppliers provide service-based applications or provide innovation value services to assist customers. The Recommender systems in this area play an active role to offer these applications to users. At the same time, a new algorithm for selecting and ranking such diverse applications is an important issue that attracted the attention of researchers. Ranking learning had been widely studied and globally used in data recovery. Global learning to rank methods is useful in this field. In this method, a general ranking was made between the queries extracted by a general ranking function. Then, with the help of the ELM The elaboration likelihood model, the features of these queries are learned. Based on the output of the learning model, a re-ranking is considered for items again. tentative outputs demonstrate that the suggest a way that can significantly develop the state of the art learn to rank way on score recuperation dataset.
Key words: Extreme learn machines, Recommendation system, item ranking, learn to rank; local rank context.
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