DESIGN OF HYBRIDIZED RECOMMENDATION SYSTEM ON MOVIE DATA USING CONTENT-BASED AND COLLABORATIVE FILTERING

DESIGN OF HYBRIDIZED RECOMMENDATION SYSTEM ON MOVIE DATA USING CONTENT-BASED AND COLLABORATIVE FILTERING

Abstract:
The rapid growth of digital platforms and the abundance of movie data have created a need for efficient recommendation systems to aid users in discovering relevant content. This abstract presents a design proposal for a hybridized recommendation system that combines the strengths of both content-based and collaborative filtering techniques to enhance the accuracy and diversity of movie recommendations.

The proposed system leverages content-based filtering, which analyzes movie attributes such as genre, cast, director, and plot to identify similarities and make personalized recommendations. By considering the intrinsic characteristics of movies, content-based filtering can capture users' preferences and suggest similar movies based on their individual tastes.

Additionally, the system incorporates collaborative filtering, which analyzes user behavior and preferences to uncover patterns and generate recommendations. Collaborative filtering utilizes the collective wisdom of a user community to suggest movies that are popular among similar users. This approach enables the system to recommend movies that users with similar tastes have enjoyed, thereby increasing the likelihood of user satisfaction.

To create a hybridized recommendation system, the proposed design employs a two-step process. In the first step, content-based filtering is applied to generate a preliminary set of movie recommendations based on the user's profile and movie attributes. In the second step, collaborative filtering techniques are employed to refine the initial recommendations by incorporating the preferences and ratings of similar users.

The hybridized recommendation system enables users to benefit from the advantages of both content-based and collaborative filtering approaches. By combining the individualized approach of content-based filtering with the collective intelligence of collaborative filtering, the system aims to provide more accurate and diverse movie recommendations that align with users' preferences.

The design proposal also considers the challenges associated with the implementation of the hybridized recommendation system, such as data sparsity, scalability, and cold-start problems. Various techniques, including matrix factorization, neighborhood-based methods, and feature engineering, are explored to address these challenges and enhance the efficiency and effectiveness of the recommendation system.

Overall, the proposed design of a hybridized recommendation system integrating content-based and collaborative filtering techniques offers a promising approach to enhance movie recommendations. By exploiting both movie attributes and user behavior, the system strives to deliver personalized and diverse movie suggestions, improving user satisfaction and engagement in the movie-watching experience.

DESIGN OF HYBRIDIZED RECOMMENDATION SYSTEM ON MOVIE DATA USING CONTENT-BASED AND COLLABORATIVE FILTERING. GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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