Download System Configuration

RELATIONAL COLLABORATIVE TOPIC REGRESSION FOR
RECOMMENDER SYSTEMS
ABSTRACT:
Due to its successful application in recommender systems, collaborative filtering (CF) has
become a hot research topic in data mining and information retrieval. In traditional CF methods,
only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit
feedback on the items given by users, is used for training and prediction. Typically, the feedback
matrix is sparse, which means that most users interact with few items. Due to this sparsity
problem, traditional CF with only feedback information will suffer from unsatisfactory
performance. Recently, many researchers have proposed to utilize auxiliary information, such as
item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic
regression (CTR) is one of these methods which has achieved promising performance by
successfully integrating both feedback information and item content information. In many real
applications, besides the feedback and item content information, there may exist relations (also
known as networks) among the items which can be helpful for recommendation. In this paper,
we develop a novel hierarchical Bayesian model called Relational Collaborative Topic
Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback
information, item content information, and network structure among items into the same model.
Experiments on real-world datasets show that our model can achieve better prediction accuracy
than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn
good interpretable latent structures which are useful for recommendation.
EXISTING SYSTEM:
Previous work on string transformation can be categorized into two groups. Some work mainly
considered efficient generation of strings. Other work tried to learn the model with different
approaches. However, efficiency is not an important factor taken into consideration in these
methods. The existing work is not focus on enhancement of both accuracy and efficiency of
string transformation.
Further Details Contact: A Vinay 9030333433, 08772261612
Email: [email protected] | www.takeoffprojects.com
Problems in existing system:
1. Not getting accurate results.
2. Time taking more to search.
3. There is no key word searching in existing system.
PROPOSED SYSTEM:
String transformation has many applications in data mining, natural language processing,
information retrieval, and bioinformatics. String transformation has been studied in different
specific tasks such as database record matching, spelling error correction, query reformulation
and synonym mining. The major difference between our work and the existing work is that we
focus on enhancement of both accuracy and efficiency of string transformation.
Advantages in proposed system:
1. Giving accurate and efficient results.
2. It takes less time to search.
3. Here we are finding key word searching, error checking, spelling checking, we are
finding synonyms and antonyms. So it is easy to check string results.
System Configuration:Hardware Configuration: Processor
 Speed
-
Pentium –IV
1.1 Ghz
 RAM
-
256 MB(min)
 Hard Disk
-
20 GB
 Key Board
-
Standard Windows Keyboard
 Mouse
 Monitor
-
Two or Three Button Mouse
-
SVGA
Further Details Contact: A Vinay 9030333433, 08772261612
Email: [email protected] | www.takeoffprojects.com
Software Configuration:-
 Operating System
: Windows XP
 Programming Language
: JAVA
 Java Version
 Back end
: JDK 1.6 & above.
:MY SQL
Further Details Contact: A Vinay 9030333433, 08772261612
Email: [email protected] | www.takeoffprojects.com