ABSTRACT:
The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businessesor individuals). Although recommend-er systems have been well studied, there are still two challenges in the development of a recommend-er system, particularly in real-world B2B e-services:1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and 2) online users’ preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key techniquein this study is a comprehensive tree matching method,which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business datasets and the Movie Lens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially ine-business applications. This study also applies the proposed recommendation approach to the development of a web-based business partner recommender system.
PROJECT OUTPUT VIDEO:
EXISTING SYSTEM:
- The three main recommendation techniques are collaborative filtering (CF), content-based (CB) and knowledge-based (KB) techniques.
- The CF technique is currently the most successful and widely used technique for recommender systems.
- CB recommendation techniques recommend items that are similar to those previously preferred by a specific user.
- The KB recommender systems offer items to users based on knowledge about the users and items
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.
- Excessive amounts of information on the Web create a severe information overload problem
- 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.
- The major limitations of CB approaches are the item content dependence problem, overspecializationproblem, and new user problem
- 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.
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.
- 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.
- 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.
ADVANTAGES OF PROPOSED SYSTEM:
- The evaluation results on the Australian business dataset. It can be seen that the proposed recommendation approach in this study has the lowest MAE, the highest precision, high recall, and highest F1 measure.
- The results indicate that the fuzzy tree-structured user preference profile effectively reflects business users’ preferences, and the proposed approach is well-suited to the business application environment.
MODULES:
- Users’ Fuzzy Preferences
- User Profile Module
- User Ratings Module
- Similarity Computation Module
- Fuzzy Preference Recommendation Module
MODULES DESCSRIPTION:
User’sFuzzy Preferences
- To make a recommendation to a user, the information about the user’s preferences must be known. The modeling method for user’s preferences is presented in this section.Information about user preferences can essentially be obtained in two different ways: extensionally and intentionally. The extensionally expressed preference information refers to information that is based on the actions or past experiences of the user with respect to specific items. The intentionally expressed preference information refers to specifications bythe user of what they desire in the items under consideration.In this paper, the user preference model covers both kinds of information. In the practice of recommender systems, a business user’s preferences are usually complex and vague. It might be difficult to require a business user to express a crisp preference for an item or a feature of an item, and it is therefore difficult to represent the user’s preferences with crisp numbers. In this study, fuzzy set techniques are used to describe users’ complex and vague preferences.
User Profile Module
- In this module, we collect user profile information such as Name, age, gender etc. To evaluate and propose our model we develop online movie recommender services. In this online movie recommender services consists of admin and User modules. Where the admin can upload the movies, with their details. Can view user details. Can delete the movies etc. User has to register first to access the recommendation model. After registering user gets access to the system, where all the movies information are updates.
User Ratings Module
- Users’ preferences or items’ reputations are drifting, thus we have to deal with the dynamic nature of data to enhance the precision of recommendation algorithms, and recent ratings and remote ratings should have different weights in the prediction.
- So we propose a set of dynamic features to describe users’multi-phase preferences in consideration of computation, flexibility and accuracy. It is impossible to learn weights of all ratings for each user, but it is possible to learn the general weights of ratings in the user’s different phases of interest if the phases include ranges of time that are long enough. In this module, user can rate to the movies by clicking the movie which they interested.
Similarity Computation Module
- Users’ preferences or items’ reputations are drifting, thus we have to deal with the dynamic nature of data to enhance the precision of recommendation algorithms, and recent ratings and remote ratings should have different weights in the prediction.
- For the sparsity of recommendation data, the main difficulty of capturing users’ dynamic preferences is the lack of useful information, which may come from three sources – user profiles, item profiles and historical rating records. Traditional algorithms heavily rely on the co-rate relation (to the same item by different users or to different items by the same user), which is rare when the data is sparse. Useful ratings are discovered using the co-rate relation, which is simple, intuitional and physically significant when we go one or two steps along, but it strongly limits the amount of data used in each prediction.
Fuzzy Preference Recommendation Module
- More information can be used for recommender systems by investigating the similar relation among related user profile and item content.We proposed a novel dynamic personalized recommendation algorithm for sparse data, in which more rating data is utilized in one prediction by involving more neighboring ratings through each attribute in user and item profiles. A set of dynamic features are designed to describe the preference information based on fuzzy preference recommendation technique, and finally are commendation is made by adaptively weighting the features using information in multiple phases of interest.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium IV 2.4 GHz.
- Hard Disk : 40 GB.
- Floppy Drive : 44 Mb.
- Monitor : 15 VGA Colour.
- Mouse : Logitech
- Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
- Operating system : Windows XP/7.
- Coding Language : JAVA/J2EE
- IDE : Netbeans 7.4
- Database : MYSQL