Context-aware search
At present, to deal with ambiguous queries, search engines return diverse search results in the hope of securing a user’s needs with at least one result from the first page of returned results. Due to this, users are often bombarded with results covering a whole range of topics, with only a handful...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/10356/50858 |
| _version_ | 1811680080688054272 |
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| author | Lou, Chii Hian. |
| author2 | Sun Aixin |
| author_facet | Sun Aixin Lou, Chii Hian. |
| author_sort | Lou, Chii Hian. |
| collection | NTU |
| description | At present, to deal with ambiguous queries, search engines return diverse search results in the hope of securing a user’s needs with at least one result from the first page of returned results. Due to this, users are often bombarded with results covering a whole range of topics, with only a handful relevant.
Previous studies on context-aware search focus mainly on building user profile based on the browsing behavior and query formulation. However, many have neglected customized search from explicit information. This research aims to create a customized search experience based on the context knowledge of the query.
The author proposed a novel way to deal with customizing search result given the explicit information. The author suggested using page structure summary to represent the page content and extract a list of keywords from the summary which best describe the query. Thereafter, the query is entered into a search engine together with the keywords to remove any ambiguity on the original query.
The author examined seven text extraction techniques, paired with five feature selection. Out of the 35 combination of techniques, extracting paragraphs containing the query term paired with feature select noun phrases (T5F5) has the highest relevance precision score.
The proposed system is also benchmarked against Google Suggested Searches (GoogleSS). On average, the proposed system obtained 68.8% higher relevance keywords compared to GoogleSS. In particular, T5F5 performed nearly four times higher relevance precision than GoogleSS. |
| first_indexed | 2024-10-01T03:19:22Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/50858 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:19:22Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/508582023-03-03T20:46:22Z Context-aware search Lou, Chii Hian. Sun Aixin School of Computer Engineering DRNTU::Engineering::Computer science and engineering At present, to deal with ambiguous queries, search engines return diverse search results in the hope of securing a user’s needs with at least one result from the first page of returned results. Due to this, users are often bombarded with results covering a whole range of topics, with only a handful relevant. Previous studies on context-aware search focus mainly on building user profile based on the browsing behavior and query formulation. However, many have neglected customized search from explicit information. This research aims to create a customized search experience based on the context knowledge of the query. The author proposed a novel way to deal with customizing search result given the explicit information. The author suggested using page structure summary to represent the page content and extract a list of keywords from the summary which best describe the query. Thereafter, the query is entered into a search engine together with the keywords to remove any ambiguity on the original query. The author examined seven text extraction techniques, paired with five feature selection. Out of the 35 combination of techniques, extracting paragraphs containing the query term paired with feature select noun phrases (T5F5) has the highest relevance precision score. The proposed system is also benchmarked against Google Suggested Searches (GoogleSS). On average, the proposed system obtained 68.8% higher relevance keywords compared to GoogleSS. In particular, T5F5 performed nearly four times higher relevance precision than GoogleSS. Bachelor of Engineering (Computer Science) 2012-11-22T04:16:11Z 2012-11-22T04:16:11Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/50858 en Nanyang Technological University 121 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Lou, Chii Hian. Context-aware search |
| title | Context-aware search |
| title_full | Context-aware search |
| title_fullStr | Context-aware search |
| title_full_unstemmed | Context-aware search |
| title_short | Context-aware search |
| title_sort | context aware search |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/50858 |
| work_keys_str_mv | AT louchiihian contextawaresearch |