Chunhui Liou

Date of Award


First Advisor

Kenneth Knox

Second Advisor

Bob Putz


In recent years, personalized movie recommendation systems have become increasingly important due to the vast amount of content available on streaming platforms. The objective of these systems is to suggest movies that align with a user's preferences based on their viewing history and feedback. To achieve this, various algorithms have been developed to analyze user data and generate personalized recommendations. This paper proposes a method that combines fuzzy c-means clustering, Pearson similarity, membership matrix, mean genre matrix, top-k selection, and collaborative filtering techniques to generate personalized movie recommendations. The mean genre matrix is incorporated into the clustering process to group movies with similar genre characteristics. The membership matrix, representing the degree to which a movie belongs to a particular cluster, is then used in conjunction with top-k selection to generate personalized recommendations for the user. Collaborative filtering techniques are used to improve recommendations by incorporating user feedback and preferences. By combining these techniques, the proposed approach can effectively suggest movies based on a user's interests and preferences.

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