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Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. All the details can be stored in the database whenever the admin wants to check the details about product, customer that could be stored in database. The main thing is the admin maintain the different kind of products to sale in single system through online. The customer can easily refer all the details about product and they can to buy.
The Administrator can upload the product 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.
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.
In this modules, the view graph product details and companies name.
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. DISADVANTAGES: It is becoming increasingly harder to find relevant content and what user recommends the actual thing.
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 other . 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. ADVANTAGES: (1) It is a general method, which can be utilized to many recommendation tasks on the Web. (2) It can provide latent semantically relevant results to the original information need. (3) This model provides a natural treatment for personalized recommendations. (4) The designed recommendation algorithm is scalable to very large datasets.
The main thing is the admin maintain the different kind of products to sale in single system through online. The customer can easily refer all the details about product and they can to buy. All the details can be stored in the database whenever the admin wants to check the details about product, customer that could be stored in database. Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content