News search engines are the exclusive search services for users’ news intake. Providing relevant query to a news search engine, the user gets back a single news result page consisting of various news articles aggregated from thousands of online news sources available on the World Wide Web. The availability and use of major news search engines like Bing news, Google news and Newslookup demand retrieval efectiveness evaluation of these search systems. In this paper, core retrieval models, namely, vector space model, Okapi BM25 and latent semantic indexing are used to evaluate retrieval efectiveness of news search engines for relevance efectiveness evaluation considering these models separately. Further, Monte-Carlo cross-entropy based rank aggregation technique is used to do more comprehensive relevance efectiveness evaluation by aggregating three individual rankings. Experimental results denote Google news’s performance to be better than the other two search engines.
|Number of pages||21|
|Journal||Springer Nature Operations Research Forum|
|Publication status||Published - Aug 10 2021|