ONE SIZE DOES NOT FIT ALL: TOWARDS USER AND QUERY DEPENDENT RANKING FOR WEB DATABASES

ONE SIZE DOES NOT FIT ALL: TOWARDS USER AND QUERY DEPENDENT RANKING FOR WEB DATABASES

ABSTRACT:

With the emergence of the deep Web, searching Web databases in domains such as vehicles, real estate, etc. has become a routine task. One of the problems in this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user- and/or query-independent manner.

This paper proposes a novel query- and user-dependent approach for ranking query results in Web databases. We present a ranking model, based on two complementary notions of user and query similarity, to derive a ranking function for a given user query. This function is acquired from a sparse workload comprising of several such ranking functions derived for various user-query pairs. The model is based on the intuition that similar users display comparable ranking preferences over the results of similar queries. We define these similarities formally in alternative ways and discuss their effectiveness analytically and experimentally over two distinct Web databases.

EXISTING SYSTEM:

Where a large set of queries given by varied classes of users is involved, the corresponding results should be ranked in a user- and query-dependent manner. The current sorting-based mechanisms used by web databases do not perform such ranking.

While some extensions to sql allow manual specification of attribute weights, this approach is cumbersome for most web users. Automated ranking of database results has been studied in the context of relational databases, and although a number of techniques perform query-dependent ranking, they do not differentiate between users and hence, provide a single ranking order for a given query across all users. In contrast, techniques for building extensive user profiles as well as requiring users to order data tuples.

PROPOSED SYSTEM:

v We propose a user- and query-dependent approach for ranking query results of web databases.

v We develop a ranking model, based on two complementary measures of query similarity and user similarity, to derive functions from a workload containing ranking functions for several user-query pairs.

v We present experimental results over two web databases supported by google base to validate our approach in terms of efficiency as well as quality for real-world use.

v We present a discussion on the approaches for acquiring/ generating a workload, and propose a learning method for the same with experimental results.

IMPLEMENTATION:

Implementation is the stage of the project when the theoretical design is turned out into a working system. Thus it can be considered to be the most critical stage in achieving a successful new system and in giving the user, confidence that the new system will work and be effective.

The implementation stage involves careful planning, investigation of the existing system and it’s constraints on implementation, designing of methods to achieve changeover and evaluation of changeover methods.

MODULES:

v admin login

v query-similarity

v user-similarity

v ranking process

MODULE DESCRIPTION:

ADMIN LOGIN:

In this module admin maintained various products of bike details with several databases. the databases have bike cost, color, details, and performance of bike details like gear, engine, etc., and also has enhancement details like alloys, electric start, etc.,

QUERY-SIMILARITY:

When customer login and search the bike details with specific price. Then bike details to be appeared with the customer to desire/wish. Details are displayed from different types of databases, using join query. Then, he give feedback to that product.

USER - SIMILARITY:

If he, expected more details for various product he go to search via user-similarity. It shows more details. Then he takes decision and once again search he wish. Then give another feedback to that product.

RANKING PROCESS:

If Customer, gave the feedback to all products. Then, admin count the about the passion by customer then, ranking the overall products.