ABSTRACT:
Recommender systems are becoming increasingly important to individual
users and businesses for providing personalized recommendations. However, while
the majority of algorithms proposed in recommender systems literature have
focused on improving recommendation accuracy, other important aspects of
recommendation quality, such as the diversity of recommendations, have often
been overlooked. In this paper, we introduce and explore a number of item
ranking techniques that can generate recommendations that have substantially
higher aggregate diversity across all users while maintaining comparable levels
of recommendation accuracy. Comprehensive empirical evaluation consistently
shows the diversity gains of the proposed techniques using several real-world
rating datasets and different rating prediction algorithms.
Architecture:
EXISTING SYSTEM:
There is a growing awareness
of the importance of aggregate diversity in recommender systems. Furthermore,
while, as mentioned earlier, there has been significant amount of work done on
improving individual diversity, the issue of aggregate diversity in recommender
systems has been largely untouched.
DISADVANTAGES:
It is becoming
increasingly harder to find relevant content. This problem is not only
widespread but also alarming.
PROPOSED
SYSTEM:
In real world settings,
recommender systems generally perform the following two tasks in order to
provide recommendations to each user. First, the ratings of unrated items are
estimated based on the available information (typically using known user
ratings and possibly also information about item content or user demographics) using
some recommendation algorithm. And second, the system finds items that maximize
the user’s utility based on the predicted ratings, and recommends them to the
user. Ranking approaches proposed in this paper are designed to improve the
recommendation diversity in the second task of finding the best items for each
user.
ADVANTAGES:
In particular, these
techniques are extremely efficient, because they are based on scalable
sorting-based heuristics that make decisions based only on the “local” data
(i.e., only on the candidate items of each individual user) without having to
keep track of the “global” information, such as which items have been
recommended across all users and how many times.
ALGORITHM:
RECOMMENDATION
ALGORITHM:
There exist multiple
variations of neighborhood-based CF techniques. In this paper, to estimate R*(u,
i), i.e., the rating that user u would give to item i, we
first compute the similarity between user u and other users u' using
a cosine similarity metric. Where I (u, u') represents the
set of all items rated by both user u and user u'. Based on the
similarity calculation, set N (u) of nearest neighbors of user u
is obtained. The size of set N (u) can range anywhere from 1
to |U|-1, i.e., all other users in the dataset.
Then, R*(u, i)
is calculated as the adjusted weighted sum of all known ratings R (u',
i) Here R (u) represents the average rating of user u.
A neighborhood-based CF technique can be user-based or item-based, depending on
whether the similarity is calculated between users or items, the user-based
approach, but they can be straightforwardly rewritten for the item-based
approach because of the symmetry between users and items in all
neighborhood-based CF calculations. In our experiments we used both user-based
and item-based approaches for rating estimation.
MODULES:
1.
POSTING THE OPINION
2.
RECOMMENDATION TECHNIQUE
3.
RATING PREDICTION
4.
RANKING APPROACH
POSTING
THE OPINION:
In this module, we get the opinions
from various people about business, e-commerce and products through online. The
opinions may be of two types. Direct opinion and comparative opinion. Direct
opinion is to post a comment about the components and attributes of products
directly. Comparative opinion is to post a comment based on comparison of two
or more products. The comments may be positive or negative.
RECOMMENDATION
TECHNIQUE:
However, the quality of recommendations
can be evaluated along a number of dimensions, and relying on the accuracy of
recommendations alone may not be enough to find the most relevant items for
each
User,
these studies argue that one of the goals of recommender systems is to provide
a user with highly personalized items, and more diverse recommendations result
in more opportunities for users to get recommended such items. With this
motivation, some studies proposed new recommendation methods that can increase
the diversity of recommendation sets for a given individual user. They
can give the feedback of such items.
RATING
PREDICTION:
First, the ratings of
unrated items are estimated based on the available information (typically using
known user ratings and possibly also information about item content) using some
recommendation algorithm. Heuristic techniques typically calculate
recommendations based directly on the previous user activities (e.g.,
transactional data or rating values). For each user, ranks all the predicted
items according to the predicted rating value
ranking the candidate (highly predicted) items based on their predicted rating
value, from lowest to highest (as a result choosing less popular items.
RANKING
APPROACH:
Ranking items according to
the rating variance of neighbors of a particular user for a particular item.
There exist a number of different ranking approaches that can improve
recommendation diversity by recommending items other than the ones with topmost
predicted rating values to a user. A comprehensive set of experiments was
performed using every rating prediction technique in conjunction with every
recommendation ranking function on every dataset for different number of top-N
recommendations.
SYSTEM REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard
Disk : 40 GB.
•
Floppy
Drive :
1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse :
Logitech.
•
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
•
Operating system :
Windows XP.
•
Coding Language
: ASP.Net with C#
•
Database : Sql
Server 2005.
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