A Novel Approach for Query Suggestions for Personalizing the Web
R. Lokeshkumar1, M. Shanmugapriya2, P. Sengottuvelan3
1Prof. R. Lokeshkumar, Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode (Tamil Nadu), India.
2Prof. M. Shanmugapriya, Department of Information Technology, MP Nachimuthu M.Jaganathan Engineering College, Erode (Tamil Nadu), India.
3Dr. P. Sengottuvelan, Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode (Tamil Nadu), India.
Manuscript received on 10 November 2013 | Revised Manuscript received on 18 November 2013 | Manuscript Published on 30 November 2013 | PP: 113-117 | Volume-3 Issue-6, November 2013 | Retrieval Number: F1316113613/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Web recommender systems predict the needs of web users and provide them with recommendations to personalize their pages. Such systems had been expected to have a bright future, especially in ecommerce and E-learning environments. However, although they have been intensively explored in the Web Mining and Machine learning fields, and there have been some commercialized systems, the quality of the recommendation and the user satisfaction of such systems are still not conclusive. In this paper we proposed a more robust approach that leverages search query logs for automatically identifying query groups for a number of different users and record the query logs and their respective sessions. The system uses query reformulation and click graphs which contain useful information on user behavior when searching online. Such information can be used effectively for the task of organizing user search histories into query groups. The proposed technique finds value in combining with keyword semantic similarity and filtering which applies knowledge gained from these query groups in various applications such as providing query suggestions for web personalization by favoring the ranking of search results.
Keywords: Web Mining, Collaborative Filtering, Personalization, Ranking pages, Recommended Systems.
Scope of the Article: Web Technologies