Generating viewer affinity score in an on-line social network
AssigneeLinkedIn Corporation
InventorsLiang Tang, Pannagadatta K. Shivaswamy, Nikita Igorevych Lytkin
Life of this patent
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Abstract
A relevance model is used to process an inventory of updates for a member of an on-line social network in order to select a subset of updates for presentation to the member. One of the features used as input to the relevance model is viewer affinity. The viewer affinity indicates preference of a member for a particular type or source of information and is determined using the estimated probability of the member clicking on the impression of an update and also based on a correction variable. The correction variable is generated based on information regarding previously-observed interactions of the member with the updates.
Claims20
A computer-implemented method comprising:
- accessing a focus profile representing a member in an on-line social network system, the focus profile comprising profile features;
- calculating expected click through rate (CTR) with respect to the focus profile and a certain activity type, the certain activity type represented by a coefficient vector, using the profile features and the coefficient vector;
- accessing observed data including a value indicating a number of times an update of the certain activity type was presented to the member and a number of times the member clicked on any of the updates of the certain activity type that were presented to the member;
- generating posterior distribution of a correction variable based on the observed data;
- and
- using at least one processor, calculating affinity score for the focus profile with respect to the certain activity type using the posterior distribution of the correction variable.
A computer-implemented system comprising:
- an access module, implemented using at least one processor, to access a focus profile representing a member in an on-line social network system, the focus profile comprising profile features;
- an expected CTR calculator, implemented using at least one processor, to calculate expected click through rate (CTR) with respect to the focus profile and a certain activity type, the certain activity type represented by a coefficient vector, using the profile features and the coefficient vector;
- a correction variable generator, implemented using at least one processor, to:
- access observed data including a value indicating a number of times an update of the certain activity type was presented to the member and a number of times the member clicked on any of the updates of the certain activity type that were presented to the member, and
- generate posterior distribution of a correction variable based on the observed data;
- and
- an affinity score module, implemented using at least one processor, to calculate affinity score for the focus profile with respect to the certain activity type using the posterior distribution of the correction variable.
A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising:
- accessing a focus profile representing a member in an on-line social network system, the focus profile comprising profile features;
- calculating expected click through rate (CTR) with respect to the focus profile and a certain activity type, the certain activity type represented by a coefficient vector, using the profile features and the coefficient vector;
- accessing observed data including a value indicating a number of times an update of the certain activity type was presented to the member and a number of times the member clicked on any of the updates of the certain activity type that were presented to the member;
- generating posterior distribution of a correction variable based on the observed data;
- and
- calculating affinity score for the focus profile with respect to the certain
Dependencies are inferred from the claim text.