Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system receives a plurality of content items associated with a viewing user. The online system derives a feature vector for each content item. The online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of shared layers and a plurality of separate layers. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the common features. The online system scores each content item based on predicted likelihood of each task. The online system ranks the plurality of content items based on the scoring.

Abstract: A social networking system provides a content feed to a client device associated with the user of a social networking system, the content feed including a plurality of content items. The social networking system identifies one or more missed content items, of the plurality of content items, that were part of the content feed and were not displayed by the client device. The social networking system generates a subsequent content feed that includes one or more content items that have not been previously provided to the client device and a missed feed unit that is associated with the one or more missed content items. In one embodiment, the missed feed unit, when selected, redirects the user to a new page that presents a different content feed including the identified missed content items. The social networking system provides the subsequent content feed to the client device for presentation to the user.

Abstract: For a content item with unknown tasks performed by a viewing user on an online system, the online system receives a plurality of content items associated with a viewing user. The online system derives a feature vector for each content item. The online system predicts a likelihood of interacting with each content item using a prediction model associated with a plurality of tasks. The prediction model comprises a plurality of shared layers and a plurality of separate layers. The plurality of shared layers are configured to extract common features that are shared across the plurality of tasks. Each separate layer is configured to predict likelihood of the viewing user performing a task associated with the separate layer based on the common features. The online system scores each content item based on predicted likelihood of each task. The online system ranks the plurality of content items based on the scoring.

Abstract: A social networking system provides a content feed to a client device associated with the user of a social networking system, the content feed including a plurality of content items. The social networking system identifies one or more missed content items, of the plurality of content items, that were part of the content feed and were not displayed by the client device. The social networking system generates a subsequent content feed that includes one or more content items that have not been previously provided to the client device and a missed feed unit that is associated with the one or more missed content items. In one embodiment, the missed feed unit, when selected, redirects the user to a new page that presents a different content feed including the identified missed content items. The social networking system provides the subsequent content feed to the client device for presentation to the user.

Abstract: A social networking system provides a content feed to a client device associated with the user of a social networking system, the content feed including a plurality of content items. The social networking system identifies one or more missed content items, of the plurality of content items, that were part of the content feed and were not displayed by the client device. The social networking system generates a subsequent content feed that includes one or more content items that have not been previously provided to the client device and a missed feed unit that is associated with the one or more missed content items. In one embodiment, the missed feed unit, when selected, redirects the user to a new page that presents a different content feed including the identified missed content items. The social networking system provides the subsequent content feed to the client device for presentation to the user.

Abstract: A social networking system provides a content feed to a client device associated with the user of a social networking system, the content feed including a plurality of content items. The social networking system identifies one or more missed content items, of the plurality of content items, that were part of the content feed and were not displayed by the client device. The social networking system generates a subsequent content feed that includes one or more content items that have not been previously provided to the client device and a missed feed unit that is associated with the one or more missed content items. In one embodiment, the missed feed unit, when selected, redirects the user to a new page that presents a different content feed including the identified missed content items. The social networking system provides the subsequent content feed to the client device for presentation to the user.

Abstract: In this paper, we propose a new approach to extracting question-context-answer triples from online discussion forums. More specifically, we propose a general framework based on Conditional Random Fields (CRFs) for context and answer detection, and also extend the basic framework to utilize contexts for answer detection and to better accommodate the features of forums.