Re-ranking Search results based on Relevancy weight: Approach and Evaluation
Nidhi Saxena, Akansha Singh · 2021 International Conference on Computational Performance Evaluation (ComPE) · 2021
With the availability of large amounts of information and the variety of data it becomes difficult for the end user to obtain the required data. There is therefore a need to customize all customer searches in order to obtain significant results during the search. This paper gives an approach to construct a personalized web search system for a machine where more than one user accessing search results with variation in interest. To top rank, the relevant outcome based on one's attention, User Identification Based Personalization technique, is adopted where the results obtained from any search engine are re-ranked based on relevancy rate. Re-ranking not only improves the search engine's hit rate but also helps the user to find the desired material more quickly.Further this paper performs the evaluation of the model by comparing it earlier search results. The system proved to be efficient and secure.