Abstract:
The rapid growth of online platforms and digital media has led to an overwhelming amount of content available to users. Consequently, recommendation systems have become essential in helping users discover relevant and personalized content. This study presents a hybridized recommendation system that combines the strengths of content-based filtering (CBF) and collaborative filtering (CF) techniques to improve the accuracy and effectiveness of movie recommendations.
Firstly, the content-based filtering approach analyzes the movie data by extracting relevant features such as genre, director, actors, and plot summaries. By leveraging these features, the system creates a profile for each user based on their historical preferences and generates recommendations that are similar to their preferred movies. This approach aims to capture the intrinsic characteristics of movies and provide personalized recommendations based on users’ individual tastes.
Secondly, the collaborative filtering technique utilizes the collective behavior and preferences of a large user community to generate recommendations. It identifies users with similar movie preferences and suggests movies that have been highly rated by those similar users but have not been previously watched by the target user. Collaborative filtering aims to leverage the “wisdom of the crowd” to provide recommendations based on the collective preferences of users with similar tastes.
To create a hybridized recommendation system, we integrate the outputs of the content-based and collaborative filtering approaches. This integration enables the system to offer diverse and accurate recommendations by combining the strengths of both techniques. The hybridization process can be achieved through various methods, such as weighted averaging, switching between models based on user preferences, or using one model to augment the other.
The proposed hybridized recommendation system is evaluated using a real-world movie dataset. The evaluation involves measuring the system’s performance in terms of accuracy, coverage, and user satisfaction. Comparative experiments are conducted to assess the effectiveness of the hybrid approach against content-based and collaborative filtering methods individually.
The results demonstrate that the hybridized recommendation system outperforms standalone content-based and collaborative filtering approaches in terms of recommendation accuracy and diversity. By leveraging both content and collaborative information, the hybrid system provides more precise recommendations tailored to individual user preferences while also offering serendipitous suggestions.
In conclusion, this study presents a hybridized recommendation system that combines content-based and collaborative filtering techniques to enhance movie recommendations. The proposed system leverages the strengths of both approaches, resulting in improved accuracy, coverage, and user satisfaction. The findings of this study contribute to the advancement of recommendation systems and their application in the domain of movie recommendations.
DESIGN AND IMPLEMENTATION OF A HYBRIDIZED RECOMMENDATION SYSTEM ON MOVIE DATA USING CONTENT-BASED AND COLLABORATIVE FILTERING. GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
