Microsoft Technology Licensing, LLC

Ajit Paul Singh

Christopher A Meek

Kumar Hemachandra Chellapilla

Asela J Gunawardana

Nikita Igorevych Lytkin

Anish Ramdas Nair

Runfang Zhou

Da Teng

Vladislav Tcheprasov

Nikolai Avteniev

Sachin Hosmani

Eran Leshem

Brandon Duncan

Sen Zhou

A system, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.

Next generation similar profiles

Disclosed in some examples are methods, systems, and machine-readable mediums which automatically determine network-based data sources for information ingestion and profile data completion. This method can be applied to automatically increase the library of network-based data sources utilized by the system to ingest profile information. This allows for more a complete tracking of member accomplishments and attributes and ultimately, allows for more complete member profiles. Before specific methods and systems for automatically determining network-based data sources are discussed, an overview of the process of ingesting information from network-based data sources and matching that information to members of the social networking service will be described.

Discovery of network based data sources for ingestion and recommendations

A random walk is performed over a graph, such as an augmented bipartite graph, relating to ownership data with respect to a plurality of users and items owned; the graph can provide social links between the users as well. Items can be recommended to users who do not own the items by randomly walking the graph starting at the user node to which the recommendation will be given. The random walk can step from user to user or from user to item; when an item is reached, the node can be absorbing such that the random walk terminates. The arrived item is recommended to the user. Parameters can also be provided to affect decisions made during the walk about which users to walk to and/or whether to walk to a user or an item.

Training random walks over absorbing graphs

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San Francisco, CA, United States of America

Ajit Paul Singh