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LEARNING to rank is a kind of learning based information retrieval techniques, specialized in learning a ranking model with some documents labeled with their relevancies to some queries, where the model is hopefully capable of ranking the documents returned to an arbitrary new query automatically. Different vertical search engines deal with different topicalities, document types or domain-specific features. For example, a medical search engine should clearly be specialized in terms of its topical focus, whereas a music, image or video search engine would concern only the documents in particular formats.
The Administrator can upload the mobile details as well as see the user comments and rating. The admin can manage the mobile details such as edit the mobile information and delete the mobile details.
In this module, the search consists of companies name, mobile prices, mobile features and mobile screen types. The search result is providing in the format of dynamic links. If the user clicks the dynamic link and then view the corresponding mobile details.
The user is giving comments to the particular mobile. Based on the user comment it will move to the positive or negative opinion. While entering the comments, user must enter the following details such as, user name, email id and user comment. The admin can view user comments and details.
The percentage within-one accuracy was incorporated since multiclass opinion classification, involving three or more classes, can be challenging given the relationship and subtle differences between semantically adjacent classes. Based on the user comments, the admin provides the rating for the particular mobile model.