Mining Web Graphs for
Recommendations
ABSTRACT:
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 movies, music, images, books recommendations, query
suggestions, tags recommendations, etc. No matter what types of data sources
are used for the recommendations, essentially these data sources can be modeled
in the form of various types 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 nodes
and generates 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, tag recommendations, expert finding,
image recommendations, image annotations, etc. The experimental analysis on
large data sets shows the promising future of our work.
ARCHITECTURE:
EXISTING
SYSTEM:
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
OF EXISTING SYSTEM:
It is becoming increasingly harder to find relevant
content and what user recommends the actual thing.
PROPOSED
SYSTEM:
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
Similar 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.
ADVANTAGES
OF PROPOSED SYSTEM:
(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.
ALGORITHM:
Query Suggestion Algorithm.
1. A converted bipartite graph G = (V +
∪ V ∗,E)
consists of query set V + and URL set V ∗. The two directed
edges are weighted using the method introduced in Previous section.
2: Given a query q in V +, a subgraph
is constructed by using depth-first search in G. The search stops when
the number of queries is larger than a predefined number.
3: As analyzed above, set α = 1, and without
loss of generality, set the initial heat value of query q fq(0) = 1 (the
choice of initial heat value will not affect the suggestion results). Start the
diffusion process using f(1) = eαRf(0).
4: Output the Top-K queries with the largest values
in vector f(1) as the suggestions.
MODULES:
1. Posting
the opinion
2. Image
Recommendation Technique
3. Rating
Prediction
4. Ranking
Approach
5. Collaborative Filtering
6. Query Suggestion
MODULES
DESCRIPTION:
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.
Image
Recommendation Technique:
Another interesting recommendation application on
the Web is image recommendation. Focus on recommending interesting images to
Web users based on users’ preference. Normally, these systems first ask users
to rate some images as they like or dislike, and then recommend images to the
users
Based on the tastes of the users. 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.
Collaborative Filtering:
User-based
approaches predict the ratings of active users based on the ratings of their
similar users, and item-based approaches predict the ratings of active users
based on the computed information of items similar to those chosen by the
active user.
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.
Query
Suggestion:
In order to
recommend relevant queries to Web users, a valuable technique, query
suggestion, has been employed by some prominent commercial search engines. This
extends the original query with new search terms to narrow down the scope of
the search. But different from query expansion, query suggestion aims to
suggest full queries that have been formulated by previous users so that query
integrity and coherence are preserved in the suggested queries.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
Ø
System :
Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44 Mb.
Ø
Monitor : 15 VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows XP.
Ø Coding Language : ASP.Net with C#
Ø Data Base : SQL
Server 2005
REFERENCE:
Hao Ma, Irwin King, Senior Member, IEEE, and Michael
Rung-Tsong Lyu, Fellow, IEEE, “Mining Web Graphs for Recommendations”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING, VOL. 24, NO. 6, JUNE 2012.