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| Microblog Retrieval Model Combining User Interest and Mixed Estimation |
| Wu Shufang1,2, Zhang Xiongtao1, Zhu Jie3 |
1.School of Management, Hebei University, Baoding 071000
2.College of Management and Economics, Tianjin University, Tianjin 300000
3.Department of Information Management, the Central Institute for Correctional Police, Baoding 071000 |
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Abstract With the further development of mobile internet technology, microblog retrieval has become an important part of microblog service. Considering the difference between microblog retrieval and traditional text retrieval, a new microblog retrieval model is put forward. The new model improves the prior probability and document language model estimation of the query likelihood model. To improve the document prior probability, the user’s interest blog library is obtained by quantifying the interest of users in blogs, and then the prior probability of microblog document is computed based on the proposed interest blog library. On the other hand, the information of blog contents and user interaction are mixed to obtain related blogs, which are used to smooth the original blog and achieve the mixed estimation on document language model, to effectively solve the problem of data sparseness in microblog short text. Experiments adopt the real data crawled from Sina to verify the effectiveness of our model, and experimental results demonstrate that our model outperforms some state-of-the-art models on P@15, P@30, and MRR.
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Received: 28 September 2018
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