Retrieval Effectiveness of News Search Engines: A Theoretical Framework
Mohammad Ubaidullah, Mohd. Kashif · International Journal of Computer Applications · 2018
News search has now become an important internet activity as users are switching from hard copies to online news reading.Many modern news search engines like: Google News or Bing News are available for this purpose.We propose a theoretical framework for evaluating the retrieval effectiveness of news search systems.The framework exploits supervised machine learning approach for evaluating therefore we performed retrieval effectiveness tests on a small data set consisting relevancy features-Tfidf and Latent Semantic Indexing (LSI) as well as freshness feature-publication time, extracted from 1120 query-document pairs collected from search results of Google News, to evaluate the performance of various machine learned learning to rank algorithms on NDCG and ERR metric at different cut-offs.The motive behind this work is to conduct large-scale retrieval effectiveness studies for news search engines.