Abstract: The disclosed technology relates to a system configured to receive a first input into a search interface and perform a first search based on the first input, wherein the first search is performed on a first set of content items managed by a content management system. The system further receives a second input into the search interface and performs, in response to receiving the second input, a second search based on the first input, wherein the second search is performed on a second set of content items managed by the content management system.

Abstract: The disclosed technology relates to a system configured to receive a first input into a search interface and perform a first search based on the first input, wherein the first search is performed on a first set of content items managed by a content management system. The system further receives a second input into the search interface and performs, in response to receiving the second input, a second search based on the first input, wherein the second search is performed on a second set of content items managed by the content management system.

Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.

Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.