Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Social influence has attracted tremendous attention from both academic and industrial communities due to the rapid development of online social networks. While most research has been focused on the direct influence beten peers, learning cascaded indirect influence has not been previously studied. ,  formulate the concept of cascade indirect influence based on the Independent Cascade model and then propose a novel online learning algorithm for learning the cascaded influence in the partial monitoring setting.  propose two bandit algorithms E-EXP3 and RE-EXP3 to address this problem.  theoretically prove that E-EXP3 has a cumulative regret bound of O(√T) over T, the number of time stamps.  will also show that RE-EXP3, a relaxed version of E-EXP3, achieves a better performance in practice.  compare the proposed algorithms with three baseline methods on both synthetic and real networks (ibo and AMiner). Our experimental results show that RE-EXP3 converges 100× faster than E-EXP3. Both of them significantly outperform the alternative methods in terms of normalized regret. Finally,  apply the learned cascaded influence to help behavior prediction and experiments show that our proposed algorithms can help achieve a significant improvement (10–15% by accuracy) for behavior prediction.
['Jie Zhang', 'Jiaqi Ma', 'Jie Tang']
I am writing a research paper on a idea Learning cascaded influence under partial monitoring
[{"from": "human", "value": "I am writing a research paper on a idea Learning cascaded influence under partial monitoring"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Social influence has attracted tremendous attention from both academic and industrial communities due to the rapid development of online social networks. While most research has been focused on the direct influence beten peers, learning cascaded indirect influence has not been previously studied. ,  formulate the concept of cascade indirect influence based on the Independent Cascade model and then propose a novel online learning algorithm for learning the cascaded influence in the partial monitoring setting.  propose two bandit algorithms E-EXP3 and RE-EXP3 to address this problem.  theoretically prove that E-EXP3 has a cumulative regret bound of O(\u221aT) over T, the number of time stamps.  will also show that RE-EXP3, a relaxed version of E-EXP3, achieves a better performance in practice.  compare the proposed algorithms with three baseline methods on both synthetic and real networks (ibo and AMiner). Our experimental results show that RE-EXP3 converges 100\u00d7 faster than E-EXP3. Both of them significantly outperform the alternative methods in terms of normalized regret. Finally,  apply the learned cascaded influence to help behavior prediction and experiments show that our proposed algorithms can help achieve a significant improvement (10\u201315% by accuracy) for behavior prediction."}]