Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Duplicate URLs have brought serious troubles to the whole pipeline of a search engine, from crawling, indexing, to result serving. URL normalization is to transform duplicate URLs to a canonical form using a set of rewrite rules. Nowadays URL normalization has attracted significant attention as it is lightight and can be flexibly integrated into both the online (e.g. crawling) and the offline (e.g. index compression) parts of a search engine. To deal with a large scale of bsites, automatic approaches are highly desired to learn rewrite rules for various kinds of duplicate URLs. ,  rethink the problem of URL normalization from a global perspective and propose a pattern tree-based approach, which is remarkably different from existing approaches. Most current approaches learn rewrite rules by iteratively inducing local duplicate pairs to more general forms, and inevitably suffer from noisy training data and are practically inefficient. Given a training set of URLs partitioned into duplicate clusters for a targeted bsite,  develop a simple yet efficient algorithm to automatically construct a URL pattern tree. With the pattern tree, the statistical information from all the training samples is leveraged to make the learning process more robust and reliable. The learning process is also accelerated as rules are directly summarized based on pattern tree nodes. In addition, from an engineering perspective, the pattern tree helps select deployable rules by removing conflicts and redundancies. An evaluation on more than 70 million duplicate URLs from 200 bsites shod that the proposed approach achieves very promising performance, in terms of both de-duping effectiveness and computational efficiency.
['Tao Lei', 'Rui Cai', 'Jiang-Ming Yang', 'Yan Ke', 'Xiaodong Fan', 'Lei Zhang']
I am writing a research paper on a idea A pattern tree-based approach to learning URL normalization rules
[{"from": "human", "value": "I am writing a research paper on a idea A pattern tree-based approach to learning URL normalization rules"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Duplicate URLs have brought serious troubles to the whole pipeline of a search engine, from crawling, indexing, to result serving. URL normalization is to transform duplicate URLs to a canonical form using a set of rewrite rules. Nowadays URL normalization has attracted significant attention as it is lightight and can be flexibly integrated into both the online (e.g. crawling) and the offline (e.g. index compression) parts of a search engine. To deal with a large scale of bsites, automatic approaches are highly desired to learn rewrite rules for various kinds of duplicate URLs. ,  rethink the problem of URL normalization from a global perspective and propose a pattern tree-based approach, which is remarkably different from existing approaches. Most current approaches learn rewrite rules by iteratively inducing local duplicate pairs to more general forms, and inevitably suffer from noisy training data and are practically inefficient. Given a training set of URLs partitioned into duplicate clusters for a targeted bsite,  develop a simple yet efficient algorithm to automatically construct a URL pattern tree. With the pattern tree, the statistical information from all the training samples is leveraged to make the learning process more robust and reliable. The learning process is also accelerated as rules are directly summarized based on pattern tree nodes. In addition, from an engineering perspective, the pattern tree helps select deployable rules by removing conflicts and redundancies. An evaluation on more than 70 million duplicate URLs from 200 bsites shod that the proposed approach achieves very promising performance, in terms of both de-duping effectiveness and computational efficiency."}]