Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.

Abstract: Methods and systems are disclosed herein for a context assistance system converting a reference in a content item based on context associated with the content item. For example, the context assistance system identifies a first entity referenced in the content item associated with a first context. The context assistance system then determines at least one attribute of the first entity. Next, the context assistance system identifies a second context in which the content item is to be presented. The context assistance system determines the second entity based on the second context and on the at least one attribute of the first entity. The context assistance system replaces reference to the first entity in the content item with reference to the second entity.

Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.

Abstract: Methods and systems for converting an input content item into an output content item to enhance comprehension of the message by an interlocutor, based on contexts. For example, the conversion may occur in any message service: when an interlocutor writes a message in English (or any other language), he or she might include a regional dialect (purposively or not), such as a piece of slang, that the other interlocutors may not understand, although they all generally write and understand English. In such circumstances, the regional dialect is identified and replaced with either a more globalized word or with another linguistic regionalism that is understandable for the intended interlocutor.

Abstract: Methods and systems are disclosed herein for a context assistance system converting a reference in a content item based on context associated with the content item. For example, the context assistance system identifies a first entity referenced in the content item associated with a first context. The context assistance system then determines at least one attribute of the first entity. Next, the context assistance system identifies a second context in which the content item is to be presented. The context assistance system determines the second entity based on the second context and on the at least one attribute of the first entity. The context assistance system replaces reference to the first entity in the content item with reference to the second entity.

Abstract: Methods and systems for converting an input content item into an output content item to enhance comprehension of the message by an interlocutor, based on contexts. For example, the conversion may occur in any message service: when an interlocutor writes a message in English (or any other language), he or she might include a regional dialect (purposively or not), such as a piece of slang, that the other interlocutors may not understand, although they all generally write and understand English. In such circumstances, the regional dialect is identified and replaced with either a more globalized word or with another linguistic regionalism that is understandable for the intended interlocutor.

Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.

Abstract: Methods and systems for converting an input content item into an output content item to enhance comprehension of the message by an interlocutor, based on contexts. For example, the conversion may occur in any message service: when an interlocutor writes a message in English (or any other language), he or she might include a regional dialect (purposively or not), such as a piece of slang, that the other interlocutors may not understand, although they all generally write and understand English. In such circumstances, the regional dialect is identified and replaced with either a more globalized word or with another linguistic regionalism that is understandable for the intended interlocutor.

Abstract: Methods and systems are disclosed herein for a context assistance system converting a reference in a content item based on context associated with the content item. For example, the context assistance system identifies a first entity referenced in the content item associated with a first context. The context assistance system then determines at least one attribute of the first entity. Next, the context assistance system identifies a second context in which the content item is to be presented. The context assistance system determines the second entity based on the second context and on the at least one attribute of the first entity. The context assistance system replaces reference to the first entity in the content item with reference to the second entity.

Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.

Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been updated based on information about content consumption associated with a profile. The information about content consumption may include information about consumption of portions of content items. A system generates content recommendations using the trained model. A system generates content portion recommendations based on the content recommendations and on the information about consumption of portions of content items. The system then causes to be provided the content recommendations.