Code:-
JPJA2378
Abstract:-
Existing System:-
The three main recommendation techniques are collaborative filtering (CF), content-based (CB) and knowledge-based (KB) techniques.</li> <li>The CF technique is currently the most successful and widely used technique for recommender systems.</li> <li>CB recommendation techniques recommend items that are similar to those previously preferred by a specific user.</li> <li>The KB recommender systems offer items to users based on knowledge about the users and items</li>
Disadvantages of Existing System:-
The fuzzy preferences models mentioned previously, which are represented as vectors, are not suitable to dealing with the tree-structured data in a Web-based B2B environment.</li> <li>Excessive amounts of information on the Web create a severe information overload problem</li> <li>When the number of rated items for the CS user is small, the CF-based approach cannot accurately find user neighbors using rating similarity; therefore, it failsto generate accurate recommendations.</li> <li>The major limitations of CB approaches are the item content dependence problem, overspecializationproblem, and new user problem</li> <li>The KB approach has some limitations, however, for instance, the KB approach needs to retain some information about items and users, as well as functional knowledge, to make recommendations. It also suffers from the scalability problem because it requires more time and effort to calculate the similarities in a large case base than other recommendation techniques.</li>
Proposed System:-
This study proposes a method for modeling fuzzy tree-structured user preferences, presents a tree matching method, and, based on the previous methods, develops an innovative fuzzy preference tree-based recommendation approach. The developed new approach has been implemented and applied in a business partner recommender system.</li> <li>This paper has three main contributions. From the theoretical aspect, a tree matching method, which comprehensively considers tree structures, node attributes, and weights, is developed.</li> <li>From the technical aspect, a fuzzy tree-structured user preference modeling method is developed, as well as a fuzzy preference tree-based recommendation approach for tree-structured items. From the practical aspect, the proposed methods/approaches are used to develop a Web-based B2B recommender system software known as Smart Biz Seeker, with effective results.</li>
Advantages of Proposed System:-
Hardware Requirements:-
- System : Pentium IV 2.4 GHz.</li> <li>Hard Disk : 40 GB.</li> <li>Floppy Drive : 44 Mb.</li> <li>Monitor : 15 VGA Colour.</li> <li>Mouse : Logitech</li> <li>Ram : 512 Mb.</li>
Software Requirements:-
- Operating system : Windows XP/7.</li> <li style="text-align: justify;">Coding Language : JAVA/J2EE</li> <li style="text-align: justify;">IDE : Netbeans 7.4</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:-
Rs 2000
Personalized Movie Recommendation System
Code:
JPJA2378
Abstract:
Existing System:
The three main recommendation techniques are collaborative filtering (CF), content-based (CB) and knowledge-based (KB) techniques.</li> <li>The CF technique is currently the most successful and widely used technique for recommender systems.</li> <li>CB recommendation techniques recommend items that are similar to those previously preferred by a specific user.</li> <li>The KB recommender systems offer items to users based on knowledge about the users and items</li>
Disadvantages of Existing System:
The fuzzy preferences models mentioned previously, which are represented as vectors, are not suitable to dealing with the tree-structured data in a Web-based B2B environment.</li> <li>Excessive amounts of information on the Web create a severe information overload problem</li> <li>When the number of rated items for the CS user is small, the CF-based approach cannot accurately find user neighbors using rating similarity; therefore, it failsto generate accurate recommendations.</li> <li>The major limitations of CB approaches are the item content dependence problem, overspecializationproblem, and new user problem</li> <li>The KB approach has some limitations, however, for instance, the KB approach needs to retain some information about items and users, as well as functional knowledge, to make recommendations. It also suffers from the scalability problem because it requires more time and effort to calculate the similarities in a large case base than other recommendation techniques.</li>
Proposed System:
This study proposes a method for modeling fuzzy tree-structured user preferences, presents a tree matching method, and, based on the previous methods, develops an innovative fuzzy preference tree-based recommendation approach. The developed new approach has been implemented and applied in a business partner recommender system.</li> <li>This paper has three main contributions. From the theoretical aspect, a tree matching method, which comprehensively considers tree structures, node attributes, and weights, is developed.</li> <li>From the technical aspect, a fuzzy tree-structured user preference modeling method is developed, as well as a fuzzy preference tree-based recommendation approach for tree-structured items. From the practical aspect, the proposed methods/approaches are used to develop a Web-based B2B recommender system software known as Smart Biz Seeker, with effective results.</li>
Advantages of Proposed System:
Hardware Requirements:
- System : Pentium IV 2.4 GHz.</li> <li>Hard Disk : 40 GB.</li> <li>Floppy Drive : 44 Mb.</li> <li>Monitor : 15 VGA Colour.</li> <li>Mouse : Logitech</li> <li>Ram : 512 Mb.</li>
Software Requirements:
- Operating system : Windows XP/7.</li> <li style="text-align: justify;">Coding Language : JAVA/J2EE</li> <li style="text-align: justify;">IDE : Netbeans 7.4</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:
Rs 2000
Additional Information
Tools Used:
Java
Cost:
₹Rs 2000
