Adaptive recommendation system
First Claim
1. A recommendation system, comprising:
- a logic subsystem comprising a processor; and
a data-holding subsystem holding instructions executable by the processor to;
generate for display a list of recommended items to a user, each recommended item having a different position in the list relative to each other recommended item of the list;
dynamically track a list interaction history of a user, the list interaction history detailing list attributes of recommended items with which that user interacts, wherein the list attributes are independent of content attributes of such recommended items and wherein a list attribute of the list attributes describes a position in the list corresponding to a recommended item;
for each position in the list, calculate a frequency with which the user interacts with an item in said position based on the list attribute of the list interaction history;
identify a position which the user has previously demonstrated a greatest frequency of interaction based on the frequency calculated for each respective position in the list; and
build a recommendation list with a plurality of candidate items having different recommendation confidences such that a candidate item having a highest recommendation confidence is presented in the identified position.
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Abstract
A recommendation system for optimizing content recommendation lists is disclosed. The system dynamically tracks a list interaction history of a user, which details that user'"'"'s interactions with a plurality of different lists presenting different recommended items to that user. The system automatically correlates one or more list preferences with that user based on the list interaction history, and builds a recommendation list with a plurality of candidate items having different recommendation confidences. The recommendation list is built such that each candidate item with a higher recommendation confidence is prioritized over each candidate item with a lower recommendation confidence according to the one or more list preferences correlated to that user.
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Citations
20 Claims
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1. A recommendation system, comprising:
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a logic subsystem comprising a processor; and a data-holding subsystem holding instructions executable by the processor to; generate for display a list of recommended items to a user, each recommended item having a different position in the list relative to each other recommended item of the list; dynamically track a list interaction history of a user, the list interaction history detailing list attributes of recommended items with which that user interacts, wherein the list attributes are independent of content attributes of such recommended items and wherein a list attribute of the list attributes describes a position in the list corresponding to a recommended item; for each position in the list, calculate a frequency with which the user interacts with an item in said position based on the list attribute of the list interaction history; identify a position which the user has previously demonstrated a greatest frequency of interaction based on the frequency calculated for each respective position in the list; and build a recommendation list with a plurality of candidate items having different recommendation confidences such that a candidate item having a highest recommendation confidence is presented in the identified position. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method of providing recommendations, comprising:
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generating for display a list of recommended items to a user, each recommended item having a different position in the list relative to each other recommended item of the list; dynamically tracking a list interaction history of a user, the list interaction history detailing list attributes of recommended items with which that user interacts, wherein the list attributes are independent of content attributes of such recommended items and wherein a list attribute of the list attributes describes a position in the list corresponding to a recommended item; for each position in the list, calculating a frequency with which the user interacts with an item in said position based on the list attribute of the list interaction history; identifying a position which the user has previously demonstrated a greatest frequency of interaction based on the frequency calculated for each respective position in the list; and building a recommendation list with a plurality of candidate items having different recommendation confidences such that a candidate item having a highest recommendation confidence is presented in the identified position. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification