Hierarchical classification of web search results to detect users
Pedro A. Castillo Valdivieso, Salma Gaou · International Journal of Artificial Intelligence and Soft Computing · 2017
User understanding in a web browsing session is a difficult subject, which attracts the attention of many researchers in this field. This article will facilitate a great impact on many internet-based applications. In this article, we focus on the detection and understanding of the user's intent that motivate a user to search on the web. The calculation of the similarity is preceded by the formation of the vicinity of the target item, the first method used is that of the single k-means clustering approach for items in different groups. This method had limitations because of sparsity problem. To overcome this limitation and increase the accuracy of our model, we opted for a systemic approach outcome of Group Technology. This approach provides the BEA algorithm to improve communities search. It's a new way to identify the neighbourhood and solve the problem of scalability.