Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence beten item and user factors. A basic MF model starts with an object function, which is consisted of the squared error beten original training matrix and predicted matrix as ll as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. Hover, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore,  propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method.
['Zhiyuan Zhang', 'Yun Liu', 'G. F. Xu', 'Gui Xun Luo']
I am writing a research paper on a idea Recommendation using DMF-based fine tuning method
[{"from": "human", "value": "I am writing a research paper on a idea Recommendation using DMF-based fine tuning method"}, {"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 (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence beten item and user factors. A basic MF model starts with an object function, which is consisted of the squared error beten original training matrix and predicted matrix as ll as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. Hover, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore,  propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method."}]