Abstract: A method for extracting user features based on user behaviors. The method uses webpage clickstream data of a current user to compute a path correlation between the current user and other users, selects a number of other users whose path correlation with the current user ranks among the highest, and then configures weights in connection to preset tags of websites visited by the selected other users, and computes a user correlation between the current user and the selected other users based on the preset tags and the weights. The method constructs weighted-directed graphs of webpage click paths based on click stream data, and converts computing user correlation to computing a similarity of weighted-directed graphs. The method further combines computing correlation of webpage tags to discover the user's clicking habits and personal preferences, and improve the accuracy and efficiency of user clustering.

Abstract: Embodiments of the present application relate to a method for collecting resource access information, a system for collecting resource access information, and a computer program product for collecting resource access information. A method for collecting resource access information is provided. The method includes acquiring an access request sent by a user terminal, the access request being generated by the user terminal based on a scanned information code, acquiring resource content and geographic location information of the user terminal based on the access request, determining an advertisement delivering location from a resource database that corresponds to the resource content and that is closest to the geographic location information of the user terminal, and collecting session access information relating to the advertisement delivering location.

Abstract: A method for extracting user features based on user behaviors. The method uses webpage clickstream data of a current user to compute a path correlation between the current user and other users, selects a number of other users whose path correlation with the current user ranks among the highest, and then configures weights in connection to preset tags of websites visited by the selected other users, and computes a user correlation between the current user and the selected other users based on the preset tags and the weights. The method constructs weighted-directed graphs of webpage click paths based on click stream data, and converts computing user correlation to computing a similarity of weighted-directed graphs. The method further combines computing correlation of webpage tags to discover the user's clicking habits and personal preferences, and improve the accuracy and efficiency of user clustering.