Framework for tasks suggestion on web search based on unsupervised learning techniques
Search systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user’s search task can be complex...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821001373 |
| _version_ | 1811310015265374208 |
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| author | Mohammad Alsulmi Reham Alshamarani |
| author_facet | Mohammad Alsulmi Reham Alshamarani |
| author_sort | Mohammad Alsulmi |
| collection | DOAJ |
| description | Search systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user’s search task can be complex and may not be expressed using a single search query, requiring the user to write several queries to fulfill all the aspects of his or her needs. In such scenarios, an intelligent search system would be beneficial to identify and understand the original search task issued by a user and then suggest several search tasks (in a form of key-phrases or short topics) related to the original search task. Aiming to tackle this limitation, this paper proposes a framework for applying several unsupervised learning approaches, including topic modeling and log mining. The results of applying these approaches to large user session data show that, indeed, these approaches would be applicable in search suggestion and task recommendation, reaching a significant improvement over a strong baseline. |
| first_indexed | 2024-04-13T09:52:22Z |
| format | Article |
| id | doaj.art-c8e4ac482e9e4d8db54ce999bb10ac7c |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T09:52:22Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-c8e4ac482e9e4d8db54ce999bb10ac7c2022-12-22T02:51:34ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134855255532Framework for tasks suggestion on web search based on unsupervised learning techniquesMohammad Alsulmi0Reham Alshamarani1Corresponding author.; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaSearch systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user’s search task can be complex and may not be expressed using a single search query, requiring the user to write several queries to fulfill all the aspects of his or her needs. In such scenarios, an intelligent search system would be beneficial to identify and understand the original search task issued by a user and then suggest several search tasks (in a form of key-phrases or short topics) related to the original search task. Aiming to tackle this limitation, this paper proposes a framework for applying several unsupervised learning approaches, including topic modeling and log mining. The results of applying these approaches to large user session data show that, indeed, these approaches would be applicable in search suggestion and task recommendation, reaching a significant improvement over a strong baseline.http://www.sciencedirect.com/science/article/pii/S1319157821001373Web searchInformation retrievalTopic modelingSearch log miningEvaluation |
| spellingShingle | Mohammad Alsulmi Reham Alshamarani Framework for tasks suggestion on web search based on unsupervised learning techniques Journal of King Saud University: Computer and Information Sciences Web search Information retrieval Topic modeling Search log mining Evaluation |
| title | Framework for tasks suggestion on web search based on unsupervised learning techniques |
| title_full | Framework for tasks suggestion on web search based on unsupervised learning techniques |
| title_fullStr | Framework for tasks suggestion on web search based on unsupervised learning techniques |
| title_full_unstemmed | Framework for tasks suggestion on web search based on unsupervised learning techniques |
| title_short | Framework for tasks suggestion on web search based on unsupervised learning techniques |
| title_sort | framework for tasks suggestion on web search based on unsupervised learning techniques |
| topic | Web search Information retrieval Topic modeling Search log mining Evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821001373 |
| work_keys_str_mv | AT mohammadalsulmi frameworkfortaskssuggestiononwebsearchbasedonunsupervisedlearningtechniques AT rehamalshamarani frameworkfortaskssuggestiononwebsearchbasedonunsupervisedlearningtechniques |