Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse user frequency is one of most ll known approaches to improve the accuracy of the standard collaborative filtering recommender. ,  propose a statistical attribute distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results  suggest the statistical attribute distance outperforms inverse user frequency in recommendation accuracy and scalability
['Li-Tung Weng', 'Yue Xu', 'Yuefeng Li', 'Richi Nayak']
I am writing a research paper on a idea An Improvement to Collaborative Filtering for Recommender Systems
[{"from": "human", "value": "I am writing a research paper on a idea An Improvement to Collaborative Filtering for Recommender Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse user frequency is one of most ll known approaches to improve the accuracy of the standard collaborative filtering recommender. ,  propose a statistical attribute distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results  suggest the statistical attribute distance outperforms inverse user frequency in recommendation accuracy and scalability"}]