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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Recommender systems use background data, such as historical data consisting of ratings from users, and input data, such as features of items or user ratings, to initiate a recommendation.

User preferences and item features have been represented as fuzzy sets. Recommendations are made with incomplete and uncertain information.

PROPOSED CONCEPT:-

We Proposes a method for modeling fuzzy tree-structured user preferences, presents a tree matching method, and, based on the above methods, develops an innovative fuzzy preference tree-based recommendation approach.

The proposed profile reflects user preferences effectively, and the recommendation approach demonstrates excellent performance for tree-structured items.

EXISTING ALGORITHM:-

Collaborative-filtering recommendation              algorithm

A brute force algorithm

PROPOSED ALGORITHM:- 

Conceptual similarity computation algorithm

Fuzzy preference tree merging algorithm

ALGORITHM DEFINITION:-

The process of filtering for information using collaboration among multiple agents, viewpoints, and data sources, etc.

The brute force algorithm consists in checking.

ALGORITHM DEFINITION:-

Record the maximum conceptual similarity tree mapping.

The process of the merge operation takes the reference of the fuzzy preference tree as input.

DRAWBACKS:-

Complicated tree structures.

User’s item preferences are frequently subjective and uncertain.

ADVANTAGES:-

The fuzzy tree-structured

Integrates both the user‘s extensionally     and intentionally expressed preferences.