Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The short-term reconsumption behaviors, i.e. "reconsume" the near past, account for a large proportion of people's activities every day and everywhere. ,  firstly derived four generic features which influence people's short-term reconsumption behaviors. These features re extracted with respect to different roles in the process of reconsumption behaviors, i.e. users, items and interactions. Then,  brought forward two fast algorithms with the linear and the quadratic kernels to predict whether a user will perform a short-term reconsumption at a specific time given the context. The experimental results show that our proposed algorithms are more accurate in the prediction tasks compared with the baselines. Meanwhile, the time complexity of online prediction of our algorithms is O(1), which enables fast prediction in real-world scenarios. The prediction contributes to more intelligent decision-making, e.g. potential revisited customer identification, personalized recommendation, and information re-finding.
['Jun Chen', 'Chaokun Wang', 'Jianmin Wang']
I am writing a research paper on a idea Will you reconsume the near past? fast prediction on short-term reconsumption behaviors
[{"from": "human", "value": "I am writing a research paper on a idea Will you reconsume the near past? fast prediction on short-term reconsumption behaviors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The short-term reconsumption behaviors, i.e. \"reconsume\" the near past, account for a large proportion of people's activities every day and everywhere. ,  firstly derived four generic features which influence people's short-term reconsumption behaviors. These features re extracted with respect to different roles in the process of reconsumption behaviors, i.e. users, items and interactions. Then,  brought forward two fast algorithms with the linear and the quadratic kernels to predict whether a user will perform a short-term reconsumption at a specific time given the context. The experimental results show that our proposed algorithms are more accurate in the prediction tasks compared with the baselines. Meanwhile, the time complexity of online prediction of our algorithms is O(1), which enables fast prediction in real-world scenarios. The prediction contributes to more intelligent decision-making, e.g. potential revisited customer identification, personalized recommendation, and information re-finding."}]