A comprehensive review from hyperlink to intelligent technologies based personalized search systems. Issue 4 (2nd October 2019)

• Record Type: Journal Article
• Authors: Malhotra, Dheeraj; Rishi, O. P.
• Is Part Of: Journal of management analytics. Volume 6:Issue 4(2019)
• Page Start: 365
• Page End: 389
• Publication Date: 2019-10-02
• Subjects: web search personalization -- meta search tool -- machine learning -- big data analytics -- collaborative filtering -- logistic regression; Management -- Mathematical models -- Periodicals; Management -- Periodicals; Management -- Mathematical models; Management; Periodicals; 658.4033
• DOI: 10.1080/23270012.2019.1671241
• Languages: English
• ISSNs: 2327-0012

Abstract

Abstract : In the present era of big data, web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines. Even though a large number of web search techniques have been developed, some problems still exist while searching with generic search engines as none of the search engines can index the entire web. The issue is not just the volume but also the relevance concerning the user's requirements. Moreover, if the search query is partially incomplete or is ambiguous, then most of the modern search engines tend to return the result by interpreting all possible meanings of the query. Concerning search quality, more than half of the retrieved web pages have been reported to be irrelevant. Hence web search personalization is required to retrieve search results while incorporating the user's interests. In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them. The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System (IMSS) and Advanced Cluster Vector Page Ranking (ACVPR) algorithm as one of the best approaches as proposed in the literature for web search personalization. Furthermore, the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas.