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; and
geolocation data indicating a current location of a client device of the particular member at a time interval corresponding to the particular member'"'"'s interaction with the various email content;
encoding the data accessed from the one or more 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.
23 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; and geolocation data indicating a current location of a client device of the particular member at a time interval corresponding to the particular member'"'"'s interaction with the various email content; encoding the data accessed from the one or more 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 processors; and a memory comprising instructions that, when executed 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 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 members interactions with various email content; and geolocation data indicating a current location of a client device of the particular member at a time interval corresponding to the particular member'"'"'s interactions with the various email content; encode the data accessed from the one or more 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 interval, 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; and geolocation data indicating a current location of a client device of the particular member at a time interval corresponding to the particular member'"'"'s interactions with the various email content; encoding the data accessed from the one or more 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