Patent ID: 6993586

Claim:
A 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, the optimal information goal being based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence, the optimal information goal being predicted as follows: recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information 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, a prediction of the optimal information goal being based on the respective probabilities, the calculating comprising: Pr ( w i ⁢  w 1 , ⩓ , w i - 1 ) ≈ Pr ⁡ ( w i | w i - n + 1 , … , w i - 2 , w i - 1 ) = Pr ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 , w i ) Pr ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 ) = C ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 , w i ) / C n C ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 ) / C n - 1 = C ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 , w i ) C ⁡ ( w i - n + 1 , … , w i - 2 , w i - 1 ) * C ; 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.