Movie recommendation systems have become increasingly popular in recent years, with the rise of online streaming platforms such as Netflix and Amazon Prime. These sys-tems aim to suggest movies to users that they are likely to enjoy based on their past preferences.
However, the traditional movie recommendation systems that use either collaborative filtering (CF) or content-based filtering (CBF) techniques have several limitations that can negatively affect the quality and diversity of recommendations. CF relies on user behavior data to recommend movies, which can lead to the problem of the cold start, where new users or movies have no historical data to rely on. Additionally, this technique often leads to the problem of the popularity bias, where popular movies receive more recommendations regardless of their quality. CBF, on the other hand, recommends movies based on their features, which can lead to limited diversity in recommendations and a lack of exposure to new and different types of movies.
To address these limitations, this research proposes the development of a hybrid movie recommendation system that combines CF and CBF techniques that can address the limitations of traditional recommendation systems, evaluate the performance of the proposed hybrid system, and compare it to traditional CF and CBF techniques. The MovieLens 1M dataset from GroupLens Research will be used to train and evaluate the system. This dataset contains ratings from over 6,040 users on approximately 3,900 movies, making it a rich source of data for building a movie recommendation system.
The proposed system will utilize the strengths of both CF and CBF techniques to provide more accurate and diverse recommendations. CF will be used to identify similar users and movies based on past behavior, while CBF will be used to analyze movie attributes such as genre, director, and cast to make recommendations. By combining these two techniques, we hope to overcome some of the limitations of each method and provide a more comprehensive and personalized recommendation system.
Dateset source (Citation): F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
The problem statement for building a movie recommenda-tion system is that the system may encounter challenges such as data sparsity, cold start problem, and lack of diver-sity. These issues can make it difficult to accurately capture user preferences and provide recommendations that are rele-vant and diverse.
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Data sparsity
Data sparsity occurs when there is a limited amount of data available for some movies, which can make it challenging to provide accurate recommendations.
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Cold start problem
The cold start problem arises when there is a lack of data available for new users, which can make it difficult to pro-vide personalized recommendations.
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Lack of diversity
The lack of diversity issue can arise when the system tends to recommend similar movies, which can limit the user's exposure to new and different content.
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Collaborative Filtering
Collaborative filtering utilizes past user behavior and pref-erences to recommend movies to users with similar tastes. This technique can help address the cold start problem by identifying similar users who have rated the same movies and providing recommendations based on their preferences. It can also address data sparsity by filling in missing data through predictions based on the preferences of similar users. Collaborative filtering can also help increase diversity by identifying less popular movies that are similar to those the user has rated positively.
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Content-Based Filtering
Content-based filtering, on the other hand, utilizes information about the movie's characteristics to recommend movies to users. This technique can help address the cold start problem by recommending movies based on the user's preferences for certain genres or actors. It can also address data sparsity by providing recommendations based on a movie's specific attributes, such as plot, genre, or director. Content-based filtering can also increase diversity by recommending movies that have similar attributes but may not be as popular as those the user has already seen.
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Hybrid system
To address the challenges of data sparsity, cold start problem, and lack of diversity effectively, a hybrid system that combines collaborative filtering and content-based filtering can be used. This approach leverages the strengths of both techniques and can help mitigate the limitations of each. A hybrid system can provide more accurate recommendations, increase diversity, and handle data sparsity and the cold start problem more effectively by incorporating information about both user preferences and movie attributes.