Aol Inc.

Livingsocial, Inc.

Microsoft Technology Licensing, LLC

Elena Zheleva

Joseph John Pfeiffer, Iii

Natasa Milic-Frayling

John Guiver

Eduarda Mendes Rodrigues

An online incentive network is optimized. Online incentive network data is accessed including data regarding sharing between users of the network from at least one database. Inputted parameters and network data are applied to one or more models for incentivized deal sharing to determine profitability of an incentivized sharing deal to offer to plurality of users via an online network for one or more products or services provided by a merchant. The incentivized sharing deal comprises a deal in which a user purchases a particular deal, and then when the user shares the particular deal with a threshold number of other users who also purchase the deal, the user receives an incentive. The parameters include a price of the deal; a proportion of the deal kept by the merchant or the online incentive network, and the number of incentivized users. An interface offers the incentivized sharing deal to users.

Incentivizing sharing in social networks

Methods and apparatuses for filtering electronic communications in a communication system. The method includes receiving a message report from a user in response to an electronic message received by the user, and identifying a confidence value associated with the user from whom the message report is received. The method also includes adding, if the confidence value exceeds a predetermined confidence value threshold, the confidence value to a signature value associated with the electronic message, and determining if the signature value exceeds a signature value threshold. The method further includes filtering the electronic message if the signature value exceeds the signature value threshold.

Systems and methods for reporter-based filtering of electronic communications and messages

Recommending a media item may include, for example, a statistical model of media consumption is applied to media session consumption data from a community of users to infer parameters of the model. The model comprises a first probability distribution for each user defining a likelihood of the user having a latent characteristic for a session, and a second probability distribution for each latent characteristic defining a likelihood of a user selecting a media item given the latent characteristic. In another example, the inferred parameters are provided to a recommendation engine arranged to recommend media items. The recommendation engine uses the model with inferred parameters and data describing media items newly consumed by a user to infer a current latent characteristic for a current session of the user, and uses them to generate recommended media items for the user in the current session based on the current latent characteristic.

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Washington, DC, United States of America

Elena Zheleva