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Collaborative filtering algorithm is a commonly used algorithm in recommendation system. However, if you want to realize personalized movie recommendation system and make innovations, you can consider the following directions to improve or replace the algorithm:
1. Deep learning algorithm: You can try to use deep learning algorithm, such as neural network, for movie recommendation. Neural network models can be used to learn the characteristics of users and movies, and to predict the movies that users are likely to be interested in from the model.
2. Reinforcement learning algorithm: reinforcement learning algorithm can be used to make personalized recommendation to users. This approach can optimize the recommendation results based on user feedback, thus achieving more personalized recommendations.
3. Content-based recommendation algorithm: It can be considered to use content-based recommendation algorithm, which can be recommended according to the attributes of the film, such as director, actor, type, etc. This algorithm can better understand the similarities and correlations between movies, allowing for more personalized recommendations.
4. Social network algorithm: Social network algorithm can be used to recommend movies by analyzing users' behaviors in social network, such as following, liking, commenting, etc. This approach allows for a better understanding of users' interests and preferences, leading to more personalized recommendations.
In short, for the movie recommendation system, different algorithms and methods can be used to improve and optimize, so as to achieve more personalized recommendation results.