Abstract: In some implementations, a computing system can deliver personalized content while preserving user privacy. For example, the computing system can include a content server that filters, deduplicates, and generates clusters of content items (e.g., articles, news stories, etc.) that are relevant to an investment identifier received in a request from a client device. The content server can send the clusters of content items to the requesting client. Upon receiving the clusters of content items, requesting client device can evaluate the content items based on user preferences stored on the user device and select a representative content item from one or more content item clusters. The selected content item can then be presented on a display of the user device. Thus, personalization of content item selection and presentation can be performed without distributing user preference data outside of the user device thereby preserving user privacy.

Abstract: In some implementations, a computing system can determine how important an investment identifier is to a content item that mentions the investment identifier. For example, a content item may describe a story, event, etc., related to an investment identifier. The content item may mention the investment identifier by mentioning the investment identifier, proxies for the investment identifier, or other equivalents associated with the investment identifier. The computing system can determine locations in the content item where the investment identifier is mentioned and/or how frequently the investment identifier is mentioned. Based on the locations and/or frequency of mentions, the computing system can determine an importance score that represents how important the investment identifier is to the story described by the content item. The importance score can be stored in metadata for the content item and used when determining which content items to present to a user.

Abstract: In some implementations, a computing system can deliver personalized content while preserving user privacy. For example, the computing system can include a content server that filters, deduplicates, and generates clusters of content items (e.g., articles, news stories, etc.) that are relevant to an investment identifier received in a request from a client device. The content server can send the clusters of content items to the requesting client. Upon receiving the clusters of content items, requesting client device can evaluate the content items based on user preferences stored on the user device and select a representative content item from one or more content item clusters. The selected content item can then be presented on a display of the user device. Thus, personalization of content item selection and presentation can be performed without distributing user preference data outside of the user device thereby preserving user privacy.

Abstract: In some implementations, a computing system can determine how important an investment identifier is to a content item that mentions the investment identifier. For example, a content item may describe a story, event, etc., related to an investment identifier. The content item may mention the investment identifier by mentioning the investment identifier, proxies for the investment identifier, or other equivalents associated with the investment identifier. The computing system can determine locations in the content item where the investment identifier is mentioned and/or how frequently the investment identifier is mentioned. Based on the locations and/or frequency of mentions, the computing system can determine an importance score that represents how important the investment identifier is to the story described by the content item. The importance score can be stored in metadata for the content item and used when determining which content items to present to a user.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: A computing device may determine, based at least in part on one or more semantic exemplars associated with a digital object, a level of incongruity for the digital object, and output an indication of the level of incongruity for the digital object. For instance, a system may provide a web application that can support hedonic web surfing. By modeling Internet users as active information foragers instead of random surfers, a system may obtain quantitative measures of digital objects that users may find psychologically stimulating. The system may utilize a quantitative measure of the conceptual incongruity of digital objects that may predict how interesting users will find an object.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. In addition to providing the articles to the reader application, an article providing system may also determine the quality or popularity of particular articles and provide the most popular articles to users of the system. In one embodiment, the system may receive one or more anonymous interaction metrics from one or more devices connected to the system. The anonymous interaction metrics may be associated with a particular article and provide some indication of a user's engagement with the article. The system utilizes these interaction metrics or measurements to set or adjust a score or ranking associated with the particular article.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. The reader application may be customizable around one or more characteristics of the user of the device. In one embodiment, the type and number of articles provided to the device and displayed in the reader application may be based on the determination of a category or type of usage of the application is performed by the user. Further, the determination of the use of the reader application on the device is performed by and contained within the device such that usage information is not shared with overall article providing system. In another embodiment, the article providing system and/or device may determine recommendations to provide to a user of the reading application. These recommendations may be based on one or more selected interests or topics of the user of the reading application.

Abstract: Aspects of the present disclosure involve a mobile or computer reader application that obtains articles or other computer files from a central database and displays the articles to a user of the device. In addition to providing the articles to the reader application, an article providing system may also determine the quality or popularity of particular articles and provide the most popular articles to users of the system. In one embodiment, the system may receive one or more anonymous interaction metrics from one or more devices connected to the system. The anonymous interaction metrics may be associated with a particular article and provide some indication of a user's engagement with the article. The system utilizes these interaction metrics or measurements to set or adjust a score or ranking associated with the particular article.

Abstract: A computing device may determine, based at least in part on one or more semantic exemplars associated with a digital object, a level of incongruity for the digital object, and output an indication of the level of incongruity for the digital object. For instance, a system may provide a web application that can support hedonic web surfing. By modeling Internet users as active information foragers instead of random surfers, a system may obtain quantitative measures of digital objects that users may find psychologically stimulating. The system may utilize a quantitative measure of the conceptual incongruity of digital objects that may predict how interesting users will find an object.