| 摘要 |
The subject invention relates to probabilistic models that are trained from transitions among various topics of pages visited by a sample population of search users (Figure 1) In one aspect, probabilistic models of topic transitions are learned for individual users and groups of users Topic transitions for individuals versus larger groups are analyzed, wherein the relative accuracies of personal models of topic dynamics with models constructed from sets of pages drawn from similar groups and from a larger population of users are compared To exploit temporal dynamics, the accuracy of these models are tested for predicting transitions in topics of visits at increasingly more distant times in the future The models can be applied to search topic dynamics of tagged pages, and then utilized to predict topics of subsequent pages visited by users.
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