An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented process, comprising: using a computer to perform process actions for: receiving a collection of data points representing transitions between pairs of source content and destination content; extracting a plurality of features from each source content; extracting a plurality of features from each destination content; and learning a transition model that statistically explains why the transitions were made by using machine learning to train on extracted features corresponding to the pairs of source content and destination content. 2. The computer-implemented process of claim 1 wherein a generative model of the source and destination content that posits a distribution over joint latent features for each possible transition of that content is used to automatically learn the features that are then extracted from the source and destination content. 3. The computer-implemented process of claim 2 wherein one or more of the features learned by the generative model are latent semantic topics derived from the source and destination content. 4. The computer-implemented process of claim 1 wherein one or more of the extracted features are manually specified prior to being extracted. 5. The computer-implemented process of claim 1 wherein a set of candidate nuggets is automatically extracted from arbitrary source content, and wherein the transition model is used to compute an interestingness score for each of those candidate nuggets. 6. The computer-implemented process of claim 5 wherein one or more candidate nuggets are used as the basis for augmenting the arbitrary content with additional content based on the interestingness score computed for those nuggets. 7. The computer-implemented process of claim 5 wherein interestingness scores are used to learn per-user profiles for personalizing one or more applications. 8. The computer-implemented process of claim 1 wherein the pairs of source content and destination content are identified from browser log data. 9. A computing device configured to: receive browser log data including transitions between pairs of source content and destination content; apply a generative model of the source content and the destination content to each possible transition of that content to automatically learn a plurality of features; extract information corresponding to the learned features from the source content and destination content; and learn a transition model that statistically explains why the transitions were made by using machine learning to train on extracted features corresponding to the pairs of source content and destination content. 10. The computing device of claim 9 wherein one or more of the features learned by the generative model are latent semantic topics derived from the source and destination content. 11. The computing device of claim 9 wherein one or more of the features to be extracted from the source content and destination content are manually specified. 12. The computing device of claim 9 wherein a set of candidate nuggets is automatically extracted from arbitrary source content, and wherein the transition model is used to compute an interestingness score for each of those candidate nuggets. 13. The computing device of claim 12 wherein the interestingness scores are used to learn per-user profiles for personalizing one or more applications. 14. The computing device of claim 12 wherein one or more candidate nuggets are used as the basis for augmenting the arbitrary content with additional content based on the interestingness score computed for those nuggets. 15. The computing device of claim 14 wherein the additional content includes one or more advertisements. 16. A computer-readable medium having computer executable instructions stored therein, said instructions causing a computing device to execute a method comprising: receiving transition data for a plurality of pairs of source content and destination content; applying machine learning techniques to the source content and the destination content, and to possible transitions between that content, to automatically learn a plurality of features; extracting information corresponding to the learned features from the source content and destination content; and learning a transition model that statistically explains why the transitions were made by using machine learning to train on extracted features corresponding to the pairs of source content and destination content. 17. The computer-readable medium of claim 16 wherein a set of candidate nuggets is automatically extracted from arbitrary source content, and wherein the transition model is used to compute an interestingness score for each of those candidate nuggets. 18. The computer-readable medium of claim 17 wherein a predetermined number of the set of candidate nuggets having the highest interestingness scores is identified and used to create a sorted set of nuggets. 19. The computer-readable medium of claim 18 wherein the sorted set of nuggets is used as the basis for augmenting the arbitrary content with additional content relating to each of those nuggets. 20. The computer-readable medium of claim 19 wherein the additional content includes one or more advertisements.