International Business Machines Corporation

Yi Wang

Jing James Xu

Chuang Gan

Dakuo Wang

Lingfei Wu

Xuye Liu

Jalal U Mahmud

Vibha Singhal Sinha

Bei Chen

Rama Kalyani T Akkiraju

Xue Ying Zhang

Si Er Han

Ji Hui Yang

Jun Wang

Haibin Liu

Obtain, at a computing device, a segment of computer code. With a classification module of a machine learning system executing on the computing device, determine a required annotation category for the segment of computer code. With an annotation generation module of the machine learning system executing on the computing device, generate a natural language annotation of the segment of computer code based on the segment of computer code and the required annotation category. Provide the natural language annotation to a user interface for display adjacent the segment of computer code.

Learning-based automated machine learning code annotation with graph neural network

One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.

Learning-based automation machine learning code annotation in computational notebooks

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A personalized product recommendation module executing within the personalized interactive decision support system correlates at least one customer to a set of consumption preferences using a machine learning model based on a set of traits of the at least one customer to form at least one customer-to-preference correlation. The personalized product recommendation module maps a set of products to the set of consumption preferences using a consumption preferences-to-product attribute mapping data structure based on a set of attributes of the set of products to form a set of product-to-preference correlations. The personalized product recommendation module matches the at least one customer to at least one product within a set of products based on the at least one customer-to-preference correlation and the set of product-to-preference correlations to form at least one product recommendation. A visual and interactive decision support module executing within the personalized interactive decision support system presents the at least one product recommendation.

Offering personalized and interactive decision support based on learned model to predict preferences from traits

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Ann Arbor, MI, United States of America

Yi Wang