Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses.   discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph.  show equivalence beten graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.
['Markus Weimer', 'Alexandros Karatzoglou', 'Alexander J. Smola']
I am writing a research paper on a idea Improving Maximum Margin Matrix Factorization
[{"from": "human", "value": "I am writing a research paper on a idea Improving Maximum Margin 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: Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses.   discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph.  show equivalence beten graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation."}]