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This paper explains a matrix factorization-based architecture and method that provides a reliability value to each prediction/recommendation. The reliability values obtained have been put to the test, and, when applied, they show improvements in prediction and recommendation quality in different recommender systems; additionally, they provide a range of values that are understandable to users.
In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.
We then plug the actively constructed entity correspondences into a general transferred collaborative-filtering model to improve recommendation quality.
We show that our approach significantly improves recommendation quality not only for queries with a sparse tag representation but also those that are well-tagged.
Experimental results show an increased value of coverage of the recommendations provided by TRACCF without affecting recommendation quality.
The tests on different datasets show that the proposed user similarity model is suitable for the sparse data and effectively improves the prediction accuracy and the recommendation quality.
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Measures of intervention outcomes included congruence between assessed needs and case plan recommendations, quality of the content of the case plan, conveyance of case plans to community treatment providers, and cross-organizational coordination.
In order to define the extent to which GPs complied with guidelines, we developed an assessment instrument based on the main diagnostic and therapeutic recommendations (quality indicators) of the two guidelines in local use [ 16, 17].
Changes in prescribing patterns will be tracked by a set of explicit recommendations (quality indicators) developed by the authors (Table 1), which will be used to analyze changes in baseline and post-intervention prescribing patterns.
This paper examines the key design concepts and recommendation-quality results of the metric.
ESGE/ESGAR do not recommend barium enema in this setting (strong recommendation, high quality evidence).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com