Abstract: An “Interactive Text Completion System” provides various techniques for presenting a user with personalized context-based multi-word text completion suggestions via a user interface. The multi-word text completion suggestions are updated in real-time as the user types and/or selects one or more words of the completion suggestions. The Interactive Text Completion System applies a language model in combination with a document context to generate the completion suggestions. In various implementations, the language model is generated from a store of prior documents created or edited by the user. As such, the language model is personalized to individual users based on prior documents of those users. However, any desired source or corpus of existing documents can be used to generate the language model. The resulting language model offers relatively long completion suggestions (e.g., phrases consisting of a sequence of multiple words) based on the current document context.

Abstract: Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Abstract: Described herein are various techniques for automatically generating recommendations for a user based upon a social network of the user. A user can request a recommendation for a particular context, and a social network of the user can be automatically pared to create a subnetwork, wherein individuals in the subnetwork have provided ratings for the particular context and/or contexts that are in some way related to the particular context. The knowledge/ratings of individuals in the subnetwork may then be leveraged to automatically generate the recommendation for the particular context.

Abstract: Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.