Design and Implementation of a Multidimensional Data Retrieval Sorting Optimization Model

Currently, how to accurately and quickly locate required information from the massive network data, especially from the current popular social network data, is the focus of data retrieval services. Based on the traditional data retrieval sorting technology, this paper proposes a multi-dimensional data retrieval sorting optimization model, considering the characteristics of data, users and applications. Meanwhile, this paper implements this model in the system of financial microblog data retrieval. It enables the retrieval system to sort the results according to the characteristics of the microblog data, users' real query intentions and financial tendency of the system. Finally, this paper shows the basic test results, and future researches are discussed.

[1]  Susan T. Dumais,et al.  Learning user interaction models for predicting web search result preferences , 2006, SIGIR.

[2]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[3]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[4]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[5]  Weiyi Meng,et al.  Using the Structure of HTML Documents to Improve Retrieval , 1997, USENIX Symposium on Internet Technologies and Systems.

[6]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1998, SODA '98.

[7]  Yiqun Liu,et al.  Automatic search engine performance evaluation with click-through data analysis , 2007, WWW '07.

[8]  Jianzhong Li,et al.  Efficient Top-k Retrieval on Massive Data , 2015, IEEE Transactions on Knowledge and Data Engineering.

[9]  Weiyi Meng,et al.  A new study on using HTML structures to improve retrieval , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.