Microsoft Technology Licensing, LLC

Ankan Saha

Ajith Muralidharan

Shaunak Chatterjee

Aastha Jain

Kinjal Basu

Aastha Nigam

Yan Wang

Dhruv Arya

Parag Agrawal

Eric M Lawrence

Ashwin Narasimha Murthy

Ye Tu

Yiou Xiao

Chun Lo

Yafei Wang

Albert Lingfeng Cui

Bohong Zhao

Emilie De Longueau

Abdulla Mohammad Al-Qawasmeh

David Sung

Zhanglong Liu

Kathryn L Evans

Prakruthi Prabhakar

Shahdad Irajpour

In an example embodiment, tensor train decompositions are used to create large, personalized layers that are efficient for segment modeling. More particularly, rather than performing learning on an input matrix of training data that contains all segments, and then crossing this matrix with a vector for a particular segment, the matrix is mapped to an N-dimensional tensor, where each of the dimensions corresponds to one of the properties used to compose the segment, which can then be approximated by tensor train decomposition to enable efficient training and scoring.

Segment modeling for machine learning using tensor train decompositions

In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Deep representation machine learned model for heterogeneous information networks

In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Recommending edges via importance aware machine learned model

Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.

Machine learning techniques to nurture content creation

Methods, systems, and computer programs are presented for providing a user experience that facilitates navigation among different topics and articles on a social network. One method includes an operation for identifying a hierarchy of topics, each topic corresponding to a respective subject, where the hierarchy defines relationships between the topics. A first topic page for a first topic is presented in a user interface in the social network. The first topic page includes articles and first options for navigating to topic pages of topics related to the first topic. The method further includes detecting a selection of a first article. In response to detecting the selection, a first article page for the first article is presented in the user interface. The first article page includes details of the first article and second options for navigating to topic pages of topics related to the first article.

Social network navigation based on content relationships

A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.

Per-article personalized models for recommending content email digests with personalized candidate article pools

A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.

Constrained multi-slot optimization for ranking recommendations

A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.

Per-article personalized model feature transformation

The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for consuming content (such as an article) in a particular language presented (or to be presented) in a heterogeneous feed of a social network.

Determining viewer language affinity for multi-lingual content in social network feeds

The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for an article presented (or to be presented) in a heterogeneous feed of a social network.

Determining viewer affinity for articles in a heterogeneous content feed

In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.

Hashing query and job posting features for improved machine learning model performance

A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.

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San Francisco, CA, United States of America

Ankan Saha