Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating advertisement messages using generative grammar models is presented. A generative grammar model defining a message structure of requested message is accessed. The message structure includes a plurality of lexical slots. The generative grammar model includes a corpus of source data to populate each lexical slot in the plurality of lexical slots to generate an advertisement message for a product listing. An advertisement message is generated in accordance with the generative grammar model and the message is published. The advertisement message for the product listing is then transmitted to a client device.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: Systems and methods are presented for matching a buyer and a seller on a market place system and generating calibrated user profiles. In one such system a plurality of subjective estimations of value is received. The subjective estimations of value are a measure between a predetermined minimum value and a predetermined maximum value. A user profile is generated. A plurality of user actions corresponding to the plurality of subjective estimations of value is received. The user profile is calibrated based on the plurality of user actions.

Abstract: Systems and methods are presented for matching a buyer and a seller on a market place system and generating calibrated user profiles. In one such system a plurality of subjective estimations of value is received. The subjective estimations of value are a measure between a predetermined minimum value and a predetermined maximum value. A user profile is generated. A plurality of user actions corresponding to the plurality of subjective estimations of value is received. The user profile is calibrated based on the plurality of user actions.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. A generative grammar model defining a message structure of requested message is accessed. The message structure includes a plurality of lexical slots. The generative grammar model includes a corpus of source data to populate each lexical slot in the plurality of lexical slots, and a grammatical constraint for each lexical slot in the plurality of lexical slots. A message is generated in accordance with the generative grammar model and the message is published.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. A generative grammar model defining a message structure of requested message is accessed. The message structure includes a plurality of lexical slots. The generative grammar model includes a corpus of source data to populate each lexical slot in the plurality of lexical slots, and a grammatical constraint for each lexical slot in the plurality of lexical slots. A message is generated in accordance with the generative grammar model and the message is published.

Abstract: A method is disclosed for estimating a duration that an instance of subject matter-related content will remain relevant. An archive of data sources spanning a period of time is analyzed to identify past instances in time in which a subject matter was newsworthy, and the duration of each instance. On receiving an indication that a user is interested in a current instance of the subject matter, an estimated period of time that the current instance will be of interest to the user is may be determined based on the duration of the past instances. In some aspects, the archive may be a corpus of social media data compiled from a social network, with each data source including or representative of an interaction between users in the social network, and the current instance of the subject technology may include content provided to the user through a social stream.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: A user activity detection engine monitors user activity during user sessions on a publication system, and detects a change in the level of the activity of the user that predicts that the user is about to execute a transaction. The change may be an increase in the level of activity. When the user activity detection engine detects such a change, the system may make an intervention to provide personalized marketing content for display to the user in an effort to improve the probability that the user will execute a transaction, and/or also to motive the user to execute the transaction on the system site instead of moving to a competitor site in search of a different transaction.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. Consistent with some embodiments, the method may include receiving a request to generate a message, which in an example embodiment is to be published to a social network platform. In response to receiving the request, a generative grammar model defining the structure of the message is accessed. The generative grammar model may include a number of blanks and may specify a source along with a grammatical constraint for a term to populate each blank. The method may further include generating the message in accordance with the generative grammar model, and causing the generated message to be published.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. Consistent with some embodiments, the method may include receiving a request to generate a message, which in an example embodiment is to be published to a social network platform. In response to receiving the request, a generative grammar model defining the structure of the message is accessed. The generative grammar model may include a number of blanks and may specify a source along with a grammatical constraint for a term to populate each blank. The method may further include generating the message in accordance with the generative grammar model, and causing the generated message to be published.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. Consistent with some embodiments, the method may include receiving a request to generate a message, which in an example embodiment is to be published to a social network platform. In response to receiving the request, a generative grammar model defining the structure of the message is accessed. The generative grammar model may include a number of blanks and may specify a source along with a grammatical constraint for a term to populate each blank. The method may further include generating the message in accordance with the generative grammar model, and causing the generated message to be published.