Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a semi-supervised learning approach for classifying program (bot) generated b search traffic from that of genuine human users. The work is motivated by the challenge that the enormous amount of search data pose to traditional approaches that rely on fully annotated training samples.  propose a semi-supervised framework that addresses the problem in multiple fronts. First,  use the CAPTCHA technique and simple heuristics to extract from the data logs a large set of training samples with initial labels, though directly using these training data is problematic because the data thus sampled are biased. To tackle this problem,  further develop a semi-supervised learning algorithm to take advantage of the unlabeled data to improve the classification performance. These two proposed algorithms can be seamlessly combined and very  cost efficient  to scale the training process. In our experiment, the proposed approach shod significant (i.e. 2:1) improvement compared to the traditional supervised approach.
['Hongwen Kang', 'Kuansan Wang', 'David Soukal', 'Fritz Behr', 'Zijian Zheng']
I am writing a research paper on a idea Large-scale bot detection for search engines
[{"from": "human", "value": "I am writing a research paper on a idea Large-scale bot detection for search engines"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a semi-supervised learning approach for classifying program (bot) generated b search traffic from that of genuine human users. The work is motivated by the challenge that the enormous amount of search data pose to traditional approaches that rely on fully annotated training samples.  propose a semi-supervised framework that addresses the problem in multiple fronts. First,  use the CAPTCHA technique and simple heuristics to extract from the data logs a large set of training samples with initial labels, though directly using these training data is problematic because the data thus sampled are biased. To tackle this problem,  further develop a semi-supervised learning algorithm to take advantage of the unlabeled data to improve the classification performance. These two proposed algorithms can be seamlessly combined and very  cost efficient  to scale the training process. In our experiment, the proposed approach shod significant (i.e. 2:1) improvement compared to the traditional supervised approach."}]