Microsoft Technology Licensing, LLC

Yaxiong Cai

Saliha Azzam

Jayaram N M Nanduri

Kiyoung Yang

Alexander V Moore

Xiaoguang Qi

Shih-Chieh Su

Kristine E Jones

Methods, systems, apparatuses, and computer program products are provided for a two-phase technique for generating content recommendations. In a first phase, a baseline recommender is configured to generate a baseline content recommendation using one or more content recommendation models, such as a Smart Adaptive Recommendations (SAR) model, Factorization Machine (FM) or Matrix Factorization (MF) models, collaborative filtering models, and/or any other machine-learning models or techniques. In a second phase, a personalized recommender implements a vector combiner configured to combine profile vectors, content vectors, and the baseline content recommendations to generate combined user vectors. A model generator may train a machine-learning model using the combined user vectors and training data comprising actual interaction behavior of the users, which may be then applied to identify a content recommendation for a particular user.

Personalization enhanced recommendation models

In non-limiting examples of the present disclosure, systems, methods and devices for optimizing machine-learned tree ensemble prediction systems are presented. A plurality of instances may be processed by a tree ensemble. Determinations regarding the expected output values of one or more nodes of the tree ensemble may be made based, at least in part, on the processed instances. Further determinations regarding the node contribution values of one or more nodes of the tree ensemble to downstream nodes may be made based on the node output values. In some examples, feature value ranges may be computed for one or more features of the tree ensemble. One or more tree ensemble optimization operations may be performed based on the information determined from one or more of the above-described operations.

Tree ensemble explainability system

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Yaxiong Cai