Literature Review of the Pioneering Approaches in Cloud-based Search Engines Powered by LETOR Techniques

TL;DR

This paper reviews recent advancements in cloud-based search engines, focusing on LETOR techniques to improve retrieval performance, deployment in cloud environments, and query expansion for enhanced user experience.

Contribution

It provides a comprehensive overview of state-of-the-art LETOR-based methods for improving cloud search engines in enterprise contexts.

Findings

Enhanced retrieval performance through LETOR techniques

Effective deployment strategies for cloud-based search platforms

Improved query expansion and suggestion methods

Abstract

Search engines play an essential role in our daily lives. Nonetheless, they are also very crucial in enterprise domain to access documents from various information sources. Since traditional search systems index the documents mainly by looking at the frequency of the occurring words in these documents, they are barely able to support natural language search, but rather keyword search. It seems that keyword based search will not be sufficient for enterprise data which is growing extremely fast. Thus, enterprise search becomes increasingly critical in corporate domain. In this report, we present an overview of the state-of-the-art technologies in literature for three main purposes: i) to increase the retrieval performance of a search engine, ii) to deploy a search platform to a cloud environment, and iii) to select the best terms in expanding queries for achieving even a higher retrieval performance as well as to provide good query suggestions to its users for a better user experience.