A cost-effective solution for blog search

作者：Chen Lin Chih^*

来源：Data & Knowledge Engineering, 2018, 116: 124-137.

DOI：10.1016/j.datak.2018.05.009

• Blog search
• Aspect model
• EM algorithm
• NGD
• Machine learning

In recent years, blogging research has grown rapidly in social networks and the number of posts has continued to grow. An effective search method for these growing posts is to use a blog search engine to help bloggers quickly and accurately find the information they need. The blog search engine faces an important problem of the short query entered by the user like the general search engine. This problem makes it difficult for search engines to correctly define the nature of user queries. Relevant literature shows that many researchers have tried to solve this problem by using different semantic analysis models. However, these models are not suitable for big data environments such as search engines because they need significant computing time. In this paper, we propose a semantic analysis model with a dynamic judgment mechanism. According to the experimental results, our model can achieve a cost-effective solution in computing time and execution performance. That is, we can use a relatively small amount of computing time to achieve a near-optimal solution performance.

• 出版日期2018-7
• 单位东华大学