RANKING NEWS FEED ITEMS USING PERSONALIZED ON-LINE ESTIMATES OF PROBABILITY OF ENGAGEMENT
First Claim
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1. A computer-implemented method comprising:
- collecting in real time engagement signals with respect to a focus update in an on-line social network system, an engagement signal represents a positive engagement with the update or an absence of engagement with the update by a member from the focus members, the focus members are those members in the on-line social network system to whom the update has been presented, the focus members represented by respective member profiles each including one or more features from a set of features, the focus update is an information item for presentation to one or more members represented by respective member profiles in the on-line social network system;
calculating, using at least one processor, in real time, for the focus update and the set of features, respective probabilities of positive engagement with the focus update by a member represented by a profile that includes a feature from the set of features, based on the collected engagement signals; and
including the calculated respective probabilities as training data for training a final pass ranker, the final pass ranker to generate a rank for each item in an inventory of updates based on features associated with a member profile in the on-line social network system.
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Abstract
An on-line social network system includes a ranker to processes an inventory of news feed updates for a member and select more relevant updates for presentation to the member. The ranker is trained using training data that includes personalized engagement probability for an update. The personalized engagement probability values are calculated in real time and for a particular update with respect to member features that appear in member profiles maintained by the on-line social network system.
16 Citations
20 Claims
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1. A computer-implemented method comprising:
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collecting in real time engagement signals with respect to a focus update in an on-line social network system, an engagement signal represents a positive engagement with the update or an absence of engagement with the update by a member from the focus members, the focus members are those members in the on-line social network system to whom the update has been presented, the focus members represented by respective member profiles each including one or more features from a set of features, the focus update is an information item for presentation to one or more members represented by respective member profiles in the on-line social network system; calculating, using at least one processor, in real time, for the focus update and the set of features, respective probabilities of positive engagement with the focus update by a member represented by a profile that includes a feature from the set of features, based on the collected engagement signals; and including the calculated respective probabilities as training data for training a final pass ranker, the final pass ranker to generate a rank for each item in an inventory of updates based on features associated with a member profile in the on-line social network system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented system comprising:
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an engagement signals collector, implemented using at least one processor, to collect engagement signals with respect to a focus update in an on-line social network system, an engagement signal represents a positive engagement with the update or an absence of engagement with the update by a member from the focus members, the focus members are those members in the on-line social network system to whom the update has been presented, the focus members represented by respective member profiles each including one or more features from a set of features, the focus update is an information item for presentation to one or more members represented by respective member profiles in the on-line social network system; a personalized engagement probability calculator, implemented using at least one processor, to calculate, in real time, for the focus update and the set of features, respective probabilities of positive engagement with the focus update by a member represented by a profile that includes a feature from the set of features, based on the collected engagement signals; and a training module, implemented using at least one processor, to include the calculated respective probabilities as training data for training a final pass ranker, the final pass ranker to generate a rank for each item in an inventory of updates based on features associated with a member profile in the on-line social network system. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising:
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collecting in real time engagement signals with respect to a focus update in an on-line social network system, an engagement signal represents a positive engagement with the update or an absence of engagement with the update by a member from the focus members, the focus members are those members in the on-line social network system to whom the update has been presented, the focus members represented by respective member profiles each including one or more features from a set of features, the focus update is an information item for presentation to one or more members represented by respective member profiles in the on-line social network system; calculating, in real time, for the focus update and the set of features, respective probabilities of positive engagement with the focus update by a member represented by a profile that includes a feature from the set of features, based on the collected engagement signals; and including the calculated respective probabilities as training data for training a final pass ranker, the final pass ranker to generate a rank for each item in an inventory of updates based on features associated with a member profile in the on-line social network system.
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Specification