System and methods for constructing personalized context-sensitive portal pages or views by analyzing patterns of users' information access activities

A computer-implemented method for building a montage, comprising:

• storing, in a memory, instructions for performing the computer-implemented method for building a montage;
• executing the instructions on a processor;
• according to the instructions being executed:
• logging past data access;
• logging context information in accordance with the past data access;
• classifying topic information;
• building a model to predict future data access of a user, the model based at least in part on the logged past data access, the logged context information, and the classified topic information, and based in part on interest of the user in a page from among the past data access, the interest in the page being computed as I(p)=L(p)*Constant1+D(p)*Constant2, where I(p) is interest in a page p, L(p) is links followed from page p, D(p) is an average number of seconds spent in sessions starting with page p, and constants Constant1 and Constant2 are selected to balance a value based on links followed with a value based on session time length;
• mining at least one of the past data access and the context information;
• and
• selecting data based on the context information.

A computer-readable medium having computer-executable instructions stored thereon to execute a data access model, comprising:

• a log that collects a plurality of past web data access patterns for a computer user, wherein each past web data access pattern comprises a web page accessed by the user on a computer and a context associated with each web page;
• a classifier component to perform automatic topic classification of the web page accessed by the user
• a context component to record the context associated with each web site;
• and
• a predictive component to determine web future data access patterns based at least in part on the past web data access patterns, the topic classification of the web page accessed by the user, and the recorded context, and based at least in part on interest of the user in a page from among the past web data access patterns, the interest in the page being computed as I(p)=L(p)*Constant1+D(p)*Constant2, where I(p) is interest in a page p, L(p) is links followed from page p, D(p) is an average number of seconds spent in sessions starting with page p, and constants Constant1 and Constant2 are selected to equate an average of links followed from page p with an average session time length of approximately 30 seconds.