PERSONALIZED DELIVERY TIME OPTIMIZATION
First Claim
1. A computer-implemented method comprising:
- determining, by a machine including a memory and at least one processor, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval;
ranking the plurality of time intervals, based on the determined likelihoods corresponding to the plurality of time intervals;
identifying a particular time interval from among the plurality of time intervals that is associated with a highest ranking; and
classifying the particular time interval as an optimum personalized message delivery time for the particular member;
wherein the determining comprises;
accessing, via one or more data sources, data including email content data describing a particular email content item and member email interaction data describing the particular member'"'"'s interactions with various email content;
encoding the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and
performing prediction modeling, based on the assembled feature vector and a trained prediction model, to predict the likelihood of the particular member performing the particular user action on the particular email content item.
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Accused Products
Abstract
Techniques for optimizing a delivery time for the delivery of messages are described. According to various embodiments, a system determines, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval. The plurality of time intervals are then ranked, based on the determined likelihoods corresponding to the plurality of time intervals. Thereafter, a particular time interval is identified from among the plurality of time intervals that is associated with a highest ranking. The particular time interval is then classified as an optimum personalized message delivery time for the particular member.
13 Citations
20 Claims
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1. A computer-implemented method comprising:
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determining, by a machine including a memory and at least one processor, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval; ranking the plurality of time intervals, based on the determined likelihoods corresponding to the plurality of time intervals; identifying a particular time interval from among the plurality of time intervals that is associated with a highest ranking; and classifying the particular time interval as an optimum personalized message delivery time for the particular member; wherein the determining comprises; accessing, via one or more data sources, data including email content data describing a particular email content item and member email interaction data describing the particular member'"'"'s interactions with various email content; encoding the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and performing prediction modeling, based on the assembled feature vector and a trained prediction model, to predict the likelihood of the particular member performing the particular user action on the particular email content item. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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a processor; and a memory comprising instructions that, when executed by the processor, cause the system to; determine, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval; rank the plurality of time intervals, based on the determined likelihoods corresponding to the plurality of time intervals; identify a particular time interval from among the plurality of time intervals that is associated with a highest ranking; and classify the particular time interval as an optimum personalized message delivery time for the particular member; wherein to determine the likelihood, the memory comprises instructions that, when executed by the processor, cause the system to; access, via one or more data sources, data including email content data describing a particular email content item and member email interaction data describing the particular member'"'"'s interactions with various email content; encode the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and perform prediction modeling, based on the assembled feature vector and a trained prediction model, to predict the likelihood of the particular member performing the particular user action on the particular email content item. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
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determining, for each of a plurality of time intervals, a likelihood of a particular member of an online social network service performing a particular member user action on a particular message content item during the corresponding time interval; ranking the plurality of time intervals, based on the determined likelihoods corresponding to the plurality of time intervals; identifying a particular time interval from among the plurality of time intervals that is associated with a highest ranking; and classifying the particular time interval as an optimum personalized message delivery time for the particular member; wherein the determining comprises; accessing, via one or more data sources, data including email content data describing a particular email content item and member email interaction data describing the particular member'"'"'s interactions with various email content; encoding the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and performing prediction modeling, based on the assembled feature vector and a trained prediction model, to predict the likelihood of the particular member performing the particular user action on the particular email content item. - View Dependent Claims (18, 19, 20)
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Specification