Verizon Media Inc.

Oath Inc.

Yahoo! Inc.

Excalibur Ip, LLC

Nathan Liu

Scott J Gaffney

Jean-Marc Langlois

Suju Rajan

Liangjie Hong

Choon Hui Teo

Yookyung Kim

Erheng Zhong

Yue Shi

Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Method and system for measuring user engagement using click/skip in content stream

The present teaching, which includes methods, systems and computer-readable media, relates to providing content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include receiving a request for content from a person; obtaining first content from a first source private to the person based on the request; obtaining second content from at least one second source based on the request; blending the first content from the first source and the second content from the at least one second source to generate a blended content; and providing the blended content to the person in response to the request.

Methods, systems and techniques for blending online content from multiple disparate content sources including a personal content source or a semi-personal content source

The present teaching, which includes methods, systems and computer-readable media, relates to ranking content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include obtaining a plurality sets of content associated with a request from a person, each of which being from a separate data source, and applying a model for each set of content to obtain a set of features for each piece of content in the set of content, wherein the model is specific to a data source from where the set of content comes from. Each set of features for each piece of content of the set of content may be normalized with respect to a common space to generate a normalized feature set. Further, a score for each piece of content from a set of content may be estimated based on the normalized feature set for the piece of content, and based on the score of the piece of content, each piece of content of the plurality sets of content may be ranked.

Methods, systems and techniques for ranking blended content retrieved from multiple disparate content sources

Briefly, embodiments of methods and/or systems for performing content recommendation are disclosed. For one embodiment, as an example, estimating relevance may include computing an inner product of latent factors corresponding to a plurality of users and features of one or more content items.

Content recommendation

Method and system for measuring user engagement using click/skip in content stream using a probability model

Method, system, and programs for mapping data. Information related to users and their interests is obtained by a first application from a second application. An interest space is determined based on one or more sources of information, each of which provides a plurality of concepts. A data structure is created with respect to the interest space, where the data structure has a plurality of attributes each of the attributes corresponds to a concept in the interest space. One or more interests for each of the users based on information obtained from the second application. Each user interest corresponds to an attribute in the structure. A user profile is generated for each user by mapping the interests of the user to the corresponding attributes in the structure.

Method and system for user profiling via mapping third party interests to a universal interest space

The present teaching relates to discovery of user unknown interests. In one example, information related to a user is retrieved from a user profile. The information indicates one or more known interests of the user. At least one known interest of the user is identified based on the information. One or more supplemental interests with respect to each identified at least one known interest of the user are identified. The one or more supplemental interests do not overlap with the one or more known interests of the user. Supplemental content associated with the one or more supplemental interests are identified. Each piece of content in the supplemental content is ranked. At least one piece of content in the supplemental content is selected based on the ranking. The selected at least one piece of supplemental content is used to discover unknown interest of the user.

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Sunnyvale, CA, United States of America

Nathan Liu