US2006010229A1PendingUtilityA1
User intention modeling for web navigation
Est. expiryMay 9, 2022(expired)· nominal 20-yr term from priority
H04L 67/564G06F 17/18H04L 9/40H04L 67/62H04L 67/63H04L 67/568G06F 16/954H04L 69/329
46
PatentIndex Score
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Cited by
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Claims
Abstract
The disclosed subject matter models or predicts a user's intention during network or WWW navigation. Specifically, a statistical multi-step n-gram probability model is used to predict a user's optimal information goal. The optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.
Claims
exact text as granted — not AI-modified1 . A computer-implemented method for modeling a user intention during network navigation, the method comprising:
predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.
2 . The method of claim 1 , wherein a content piece of the sequence comprises a Web page.
3 . The method of claim 1 , wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.
4 . The method of claim 1 , wherein predicting the optimal information goal further comprises:
recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces; for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−1 in the sequence; and wherein predicting the optimal information goal is based on the respective probabilities.
5 . The method of claim 4 , wherein the history further comprises a URL, a hyperlink, a user name, a start time, text corresponding to a URL, a query, elapsed time, or an object name.
6 . The method of claim 4 , wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−1 in the sequence is performed based on the following:
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;
wherein, Pr represents the probability;
wherein user navigation to the plurality of networked content pieces is represented as w 1 , w 2 ,Λ, w i ,Λ, w L , where w i is the ith visited content piece in the sequence; and
wherein C(w i−n+1 , . . . ,w i−2 ,w i−1 ,w i ) denotes the count of an n-Gram (w i−n+1 , . . . , w i−2 ,w i−1 ,w i ) appearing in training data, C n is a total number of the n-grams, C n−1 is a total number of the (n−1)-grams, C equals to C n /C n−1 , C n , C n− , and C are constants.
7 . The method of claim 7 , wherein the globally optimized navigation path is based on the following:
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and
,
wherein k represents a number of networked content pieces that the user has already visited.
8 . The method of claim 7 , wherein the globally optimized navigation path is based on the following:
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wherein k represents a number of networked content pieces that the user has already visited; and
wherein t indicates how many steps are predicted.
9 . The method of claim 9 further comprising dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.
10 . The method of claim 9 , wherein the optimal information goal is determined according to the following:
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.
11 . A computer-readable medium for modeling a user intention during network navigation, the computer-readable medium comprising computer-program instructions executable by a processor for:
predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.
12 . The computer-readable medium of claim 12 , wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.
13 . The computer-readable medium of claim 12 , wherein the instructions for predicting the optimal information goal further comprise instructions for:
recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces; for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−1 in the sequence; and wherein predicting the optimal information goal is based on the respective probabilities.
14 . The computer-readable medium of claim 15 , wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−1 in the sequence is performed based on the following:
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;
wherein, Pr represents the probability;
wherein user navigation to the plurality of networked content pieces is represented as w 1 ,w 2 ,Λ,w i ,Λ,w L , where w i is the ith visited content piece in the sequence; and
wherein C(w i−n+1 , . . . ,w i−2 ,w i−1 ,w i ) denotes the count of an n-Gram (w i−n+1 , . . . , w i−2 ) w i−1 ,w i ) appearing in training data, C n is a total number of the n-grams, C n−1 is a total number of the (n−1)-grams, C equals to C n /C n−1 , C n , C n− , and C are constants.
15 . The computer-readable medium of claim 17 , wherein the globally optimized navigation path is based on the following:
argmax
w
i
∏
i
=
k
+
1
∞
Pr
(
w
i
❘
w
i
-
2
w
i
-
1
)
;
and
,
wherein k represents a number of networked content pieces that the user has already visited.
16 . The computer-readable medium of claim 17 , wherein the globally optimized navigation path is based on the following:
1
t
∑
i
=
k
+
1
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=
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+
t
log
(
Pr
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i
❘
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i
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2
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1
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)
;
wherein k represents a number of networked content pieces that the user has already visited; and
wherein t indicates how many steps are predicted.
17 . The computer-readable medium of claim 20 further comprising instructions for dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.
18 . The computer-readable medium of claim 20 , wherein the optimal information goal is determined according to the following:
argmax
t
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1
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=
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+
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+
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w
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)
.
19 . A computing device for modeling a user intention during network navigation, the computing device comprising:
a processor; and a memory coupled to the processor, the memory comprising computer-program instructions that are fetched and executed by the processor for:
predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and
wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.
20 . The computing device of claim 23 , wherein the instructions for predicting the optimal information goal further comprise instructions for:
recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces; for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−1 in the sequence; and wherein predicting the optimal information goal is based on the respective probabilities.Join the waitlist — get patent alerts
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