Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems help b users to address information overload. Hover, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. ,  consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user. The proposed method is based on Matrix Factorization (MF) which is a strong prediction model for recommender systems. First, the proposed method explores the latent space to get closer to the optimal new user parameters. Then, it exploits the learned parameters and slightly adjusts them. The results show that beside improving the accuracy of recommendation, MF approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems.
['Rasoul Karimi', 'Christoph Freudenthaler', 'Alexandros Nanopoulos', 'Lars Schmidt-Thieme']
I am writing a research paper on a idea Non-myopic active learning for recommender systems based on Matrix Factorization
[{"from": "human", "value": "I am writing a research paper on a idea Non-myopic active learning for recommender systems based on Matrix Factorization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems help b users to address information overload. Hover, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. ,  consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user. The proposed method is based on Matrix Factorization (MF) which is a strong prediction model for recommender systems. First, the proposed method explores the latent space to get closer to the optimal new user parameters. Then, it exploits the learned parameters and slightly adjusts them. The results show that beside improving the accuracy of recommendation, MF approach also results in drastically reduced user waiting times, i.e., the time that the users wait before being asked a new query. Therefore, it is an ideal choice for using active learning in real-world applications of recommender systems."}]