Yahoo! Inc.

Oath Inc.

Excalibur Ip, LLC

Verizon Media Inc.

Vidit Jain

Sachin Sudhakar Farfade

Abhranil Chatterjee

Nikhil Rasiwasia

Sourangshu Bhattacharya

Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.

Content recommendations

A method and system for mining trends around trending terms. The method includes determining a plurality of articles, from one or more websites, in relation to a first entity for a time period. The first entity is a trending term. The method also includes generating comment clusters for the plurality of articles. Each comment cluster is generated for associated article and includes plurality of user comments. The method further includes extracting one or more entities from plurality of user comments for each of the comment clusters, the one or more entities related to the first entity. Further, the method includes enabling selection of a second entity, from the one or more entities, by the user. Moreover, the method includes rendering one or more user comments corresponding to the first entity and the second entity for the time period. The system includes an electronic device, communication interface, memory, and processor.

Method and system for mining trends around trending terms

Disclosed herein are a system, method and architecture for media content enrichment. A visual object detector is trained using a training data set and an existing visual object detector. The newly-adapted visual object detector may be used to detect a visual object belonging to a class of visual object. The existing object detector that is used to train the adapted object detector detects a class of visual objects different from the visual object class detected by the adapted object detector. A media content item depicting a visual object detected using the adapted object detector may be associated with metadata, tag or other information about the detected visual object to enrich the media content item.

Media content enrichment using an adapted object detector

A method and system for displaying comments associated with a query. The method includes receiving the query provided by a user, retrieving a set of comment clusters from a comment cluster database, selecting engaging comments from each comment cluster included in the set of comment clusters, aggregating the engaging comments that are obtained from each comment cluster, identifying a plurality of independent comments included in a list of aggregated comments and displaying a list of engaging comments to the user. The system includes an electronic device, a communication interface and a memory. The system also includes a processor to receive the query, retrieve a set of comment clusters, select engaging comments from each comment cluster, aggregate the engaging comments, identify a plurality of independent comments and display a list of engaging comments to the user.

Method and system for displaying comments associated with a query

A method and system for providing a structured topic drift for a displayed set of user comments on an article. The method includes determining an ordered sequence of topical recommendations based on one or more properties of the displayed set of user comments and user characteristics using a sequential recommendation model. The method also includes sampling one or more user comments for each of the topical recommendations. Further, the method includes appending, one by one, the one or more user comments for each of the topical recommendations to bottom of the displayed set of user comments. Moreover, the method includes updating the sequential recommendation model based on a user response to the one or more user comments for each of the topical recommendations. The system includes one or more electronic devices, a communication interface, a memory, and a processor.

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Bengaluru, India

Vidit Jain