Microsoft Technology Licensing, LLC

Rovi Technologies Corporation

Adam Tauman Kalai

Omer Tamuz

Sumit Gulwani

Butler W Lampson

Christian Herwarth Borgs

Jennifer Tour Chayes

Moshe Tennenholtz

Aditya Krishna Menon

Ce Liu

Seyed Vahab Mirrokni

Uriel Mordechai Feige

Ohad Shamir

Abraham D Flaxman

Serge J Belongie

Reid M Anderson

Azarakhsh Malekian

There is provided a method and system for generating a program. The method includes detecting a number of steps for performing a task on a computing device and detecting an example relating to each of the steps, wherein the example includes input data and corresponding output data relating to the step. The method also includes, for each example, determining a rule that transforms the input data to the corresponding output data based on cues including textual features within the input data and the corresponding output data. The method further includes generating a program for performing the task based on the rules.

Generating programs using context-free compositions and probability of determined transformation rules

A recommendation ranking system that computes trust for entities based on negative expressions of trust. Negative expressions of trust are used to reduce the trust of entities. However, the system may discount entities that in the aggregate are distrusted. The system may be used with a social network to provide accurate, personalized recommendations for members of the social network. The network may be modeled as a voting network, with each member of the social network represented as a node and expressions of trust between members represented as weights on edges between nodes. Values of trust may be computed for nodes in the network and used to generate a recommendation. Opinions expressed on a topic may be weighted by trust in the node expressing the opinion. The system may be applied in other settings that can be modeled as a voting network, including ranking of Internet search results.

Recommendation ranking system with distrust

A method, system, and computer-readable storage medium for computing a representation of similarity among items in a set of items. Computing a representation of similarity items may comprise generating a first similarity model that represents characteristics of the set of items, the characteristics being indicative of similarity among the items in the set of items. Additionally, computing the representation of similarity may comprise adaptively selecting a subset of the set of items for similarity evaluation based on the first similarity model, receiving a similarity evaluation for the adaptively-selected subset of items, and generating a second similarity model based on the first to similarity model and the received similarity evaluation.

Adaptively learning a similarity model

Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Trust-based recommendation systems

Growing community of inventors

Atlanta, GA, United States of America

Adam Tauman Kalai