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| LSG: A unified multi-dimensional latent semantic graph for personal information retrieval | |
| Huangfu, Yang (1); Liu, Kuien (1); Zhang, Wen (1); Zhou, Peng (1); Wu, Yanjun (1); Wang, Qing (1); Zhu, Jia (4) | |
| 2014 | |
| Conference Name | 15th International Conference on Web-Age Information Management, WAIM 2014 |
| Pages | 540-552 |
| Conference Date | June 16, 2014 - June 18, 2014 |
| Conference Place | Macau, China |
| Indexed Type | CPCI ; EI |
| Publish Place | Springer Verlag |
| ISSN | 3029743 |
| ISBN | 9783319080093 |
| Department | (1) Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China; (2) University of Chinese Academy of Sciences, Beijing, 100190, China; (3) State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China; (4) School of Computer Science, South China Normal University, Guangzhou, 510631, China |
| English Abstract | Traditional desktop search engines can merely support keywordbased search as they don't utilize any other information, such as contextual/ semantic information, which has been commonly used in internet search. We observe that a user usually operates some files to complete a task related to a certain topic and organizes these files in some directories. Inspired by the observation, we propose an approach that considers three relations among personal files to improve desktop search, namely Topic, Task and Location. Each relation is derived from topics of files, user activities log and hierarchy of file system respectively. The heart of our approach is Latent Semantic Graph (LSG), which can measure the three relations with associated score. Based on LSG, we develop a personalized ranking schema to improve traditional keyword- based desktop search and design a novel recommendation algorithm to expand search results semantically. Experiments reveal that the performance of proposed approach is superior to that of traditional keyword-based desktop search. © 2014 Springer International Publishing Switzerland.; Traditional desktop search engines can merely support keywordbased search as they don't utilize any other information, such as contextual/ semantic information, which has been commonly used in internet search. We observe that a user usually operates some files to complete a task related to a certain topic and organizes these files in some directories. Inspired by the observation, we propose an approach that considers three relations among personal files to improve desktop search, namely Topic, Task and Location. Each relation is derived from topics of files, user activities log and hierarchy of file system respectively. The heart of our approach is Latent Semantic Graph (LSG), which can measure the three relations with associated score. Based on LSG, we develop a personalized ranking schema to improve traditional keyword- based desktop search and design a novel recommendation algorithm to expand search results semantically. Experiments reveal that the performance of proposed approach is superior to that of traditional keyword-based desktop search. © 2014 Springer International Publishing Switzerland. |
| Keyword | Latent Semantic Discovery Graph Model Information Retrieval |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16516 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Huangfu, Yang ,Liu, Kuien ,Zhang, Wen ,et al. LSG: A unified multi-dimensional latent semantic graph for personal information retrieval[C]. Springer Verlag,2014:540-552. |
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