LSG: A unified multi-dimensional latent semantic graph for personal information retrieval
文献类型：会议论文
| 作者 | Huangfu, Yang (1) ; Liu, Kuien (1) ; Zhang, Wen (1) ; Zhou, Peng (1) ; Wu, Yanjun (1) ; Wang, Qing (1) ; Zhu, Jia (4) |
| 出版日期 | 2014 |
| 会议名称 | 15th International Conference on Web-Age Information Management, WAIM 2014 |
| 会议日期 | June 16, 2014 - June 18, 2014 |
| 会议地点 | Macau, China |
| 关键词 | Latent Semantic Discovery Graph Model Information Retrieval |
| 页码 | 540-552 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | CPCI ; EI |
| 会议录出版地 | Springer Verlag |
| 语种 | 英语 |
| ISSN号 | 3029743 |
| ISBN号 | 9783319080093 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16516]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Huangfu, Yang ,Liu, Kuien ,Zhang, Wen ,et al. LSG: A unified multi-dimensional latent semantic graph for personal information retrieval[C]. 见:15th International Conference on Web-Age Information Management, WAIM 2014. Macau, China. June 16, 2014 - June 18, 2014. |
入库方式： OAI收割
来源：软件研究所
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