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.
No comments:
Post a Comment