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As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including music, images, books recommendations, query suggestions, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of graphs. In this paper, aiming at providing a general framework on mining Web graphs for recommendations, (1) we first propose a novel diffusion method which propagates similarities between different recommendations; (2) then we illustrate how to generalize different recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, image recommendations, etc. The experimental analysis on large datasets shows the promising future of our work.
The Administrator can upload the mobile details as well as see the user comments and add item. The admin can manage the Product details such as edit the product information and delete the product details.
In this module each user had separate login. The user must register their details to login. Already registered user can login directly otherwise they must register to access the service provided by the admin. After searching the product user can give the product to admin. Using the customer Product add the admin can improve the product quality or anything else.
In this module, the search consists of View the Product details companies name, Product prices,Product 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 Product details.
The last challenge is that it is time-consuming and inefficient to design different recommendation algorithms for different recommendation tasks. Actually, most of these recommendation problems have some common features, where a general framework is needed to unify the recommendation tasks on the Web. Moreover, most of existing methods are complicated and require tuning a large number of parameters.
In order to satisfy the information needs of Web users and improve the user experience in many Web applications, Recommender Systems. This is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. The underlying assumption of collaborative filtering is that the active user will prefer those items which otherSimilar users prefer the proposed method consists of two stages: generating candidate queries and determining “generalization/specialization” relations between these queries in a hierarchy. The method initially relies on a small set of linguistically motivated extraction patterns applied to each entry from the query logs, then employs a series of Web-based precision-enhancement filters to refine and rank the candidate attributes.