By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Fresh data on Information Technology are presented in a new report. According to news reporting originating from Glasgow, United Kingdom, by VerticalNews correspondents, research stated, "Several questions remain unanswered by the existing literature concerning the deployment of query-dependent features within learning to rank. In this work, we investigate three research questions in order to empirically ascertain best practices for learning-to-rank deployments. (i) Previous work in data fusion that pre-dates learning to rank showed that while different retrieval systems could be effectively combined, the combination of multiple models within the same system was not as effective."
Our news editors obtained a quote from the research from the University of Glasgow, "In contrast, the existing learning-to-rank datasets (e. g., LETOR), often deploy multiple weighting models as query-dependent features within a single system, raising the question as to whether such a combination is needed. (ii) Next, we investigate whether the training of weighting model parameters, traditionally required for effective retrieval, is necessary within a learning-to-rank context. (iii) Finally, we note that existing learning-to-rank datasets use weighting model features calculated on different fields (e. g., title, content, or anchor text), even though such weighting models have been criticized in the literature. Experiments addressing these three questions are conducted on Web search datasets, using various weighting models as query-dependent and typical query-independent features, which are combined using three learning-to-rank techniques. In particular, we show and explain why multiple weighting models should be deployed as features. Moreover, we unexpectedly find that training the weighting model's parameters degrades learned model's effectiveness."
According to the news editors, the research concluded: "Finally, we show that computing a weighting model separately for each field is less effective than more theoretically-sound field-based weighting models."
For more information on this research see: About Learning Models with Multiple Query-Dependent Features. ACM Transactions on Information Systems, 2013;31(3):207-43. ACM Transactions on Information Systems can be contacted at: Assoc Computing Machinery, 2 Penn Plaza, Ste 701, New York, NY 10121-0701, USA.
The news editors report that additional information may be obtained by contacting C. Macdonald, University of Glasgow, Glasgow G12 8QQ, Lanark, United Kingdom. Additional authors for this research include R.L.T. Santos, I. Ounis and B. He.
Keywords for this news article include: Europe, Glasgow, United Kingdom, Information Technology
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