Method for recommending short-cuts in order to ease and fasten navigating within navigation topology

• US 9,703,880 B2
• Filed: 05/18/2009
• Issued: 07/11/2017
• Est. Priority Date: 09/11/2006
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Liesche, Stefan, Nauerz, Andreas
• Primary Examiner(s): HAILU, TADESSE
• Application Number:
  US12/402,000
• Publication Number:
  US 20090222737A1
• Time in Patent Office: 2,976 Days
• Field of Search: 715738, 715742
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/56 Provisioning of proxy services store-and-forward switching systems in data switching networks H04L12/54

Abstract

Embodiments of the present invention provide a method and system for designing a Web Portal comprising a hierarchical structure of portal pages and portlets for accessing Web contents accessible via the Portal.

The method of the invention tracks the paths across which a user traverses a corresponding navigation topology of a portal in order to learn from the behavior of the user. Subsequently during future sessions in the portal when navigating at the same node of the web portal recommendations can be provided that allow the user to access links to an expected final destination page.

Claims

1. A method in a web portal environment for assisting a user during navigation in a given hierarchical navigation topology comprising nodes and edges connecting between said nodes, in which topology a web portal page (125) corresponds to a predetermined topology level and displays one or more portlets (120), said method comprising the steps of:

i) during navigation of a user in the topology of said web portal executing in at least one computer system, automatically sensing (520, 525) information about the navigational behavior of said user for each page comprised of said navigation,

ii) deriving (530) page hits and page target hits from said sensed information, wherein page hits are defined for pages clicked for reaching another page, and page target hits are defined for pages a user clicks in order to perform a page-specific task or to receive some page-specific information,

iii) using a predefined metric based on said sensed information for generating a utility ranking for the web pages comprised of the user navigation, wherein the utility ranking calculation of a page includes a time-related weighting prioritizing newly used pages,

iv) tracking (620) the current navigational position during said user'"'"'s navigation in the web portal by viewing the user'"'"'s navigation as an n-order Markov chain accounting for a current page and recent viewing history,

v) utilizing the n-order Markov chain, calculating for said current position and a plurality of target pages a probability that the user will navigate to a respective one of said target pages based upon a number of times the respective one of the target pages has been visited after having traversed a determinate path to the respective one of the target pages, wherein said target pages are obtained by said utility ranking,

vi) calculating (670) an expected saving factor for each target page based on said target page utility and on the navigational distance between said current page and said target page,

vii) defining (675) at least one shortcut including the respective direct link to at least one of the target pages having a high expected saving factor,

viii) displaying (680) said short cut as a clickable link to said user through the web portal, and,

ix) tracking which are the most followed paths for the user, and placing recommendations depending on where the user currently navigates, in order to reduce a number of clicks needed to navigate through the most popular navigation paths.

2. The method according to claim 1, wherein a page hit is defined by a first click to a page and a subsequent click being done within a predetermined time range T-hit after said first click, and a page target hit being defined by a first click to a page and a subsequent click being done after a predetermined second time range T-target-hit having passed.

3. The method according to claim 1, wherein a list of clickable shortcuts is displayed, which list is ordered according to the amount of the expected saving factor.

4. The method according to claim 1, wherein said calculation of said expected saving factor includes the product of target page utility and navigational distance.

5. The method according to claim 4, wherein said calculation of said expected saving factor includes a power of said product.

6. The method according to claim 1, wherein said shortcut is displayed within the theme of the currently displayed web page.

7. The method according to claim 1, comprising the further steps of:

a) observing a context from the group of;

a1) current date,

a2) current time,

a3) currently used device type,

b) storing respective context information for a current user session,

c) using said context information as a filter for shortcut generation and/or shortcut display.

8. The method of claim 1, wherein the n-order Markov model is a first order Markov model.

9. A computer program product for assisting a user during navigation in a given hierarchical navigation topology comprising nodes and edges connecting between said nodes, in which topology a web portal page (125) corresponds to a predetermined topology level and displays one or more portlets (120), characterized by a functional component (175), the computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code implementing the steps of:

a) during navigation of a user in the topology of said web portal automatically sensing (520, 525) information about the navigational behavior of said user for each page comprised of said navigation,

b) deriving (530) page hits and page target hits from said sensed information, wherein page hits are defined for pages clicked for reaching another page, and page target hits are defined for pages a user clicks in order to perform a page-specific task or to receive some page-specific information,

c) using a predefined metric based on said sensed information for generating a utility ranking for the web pages comprised of the user navigation, wherein the utility ranking calculation of a page includes a time-related weighting prioritizing newly used pages,

d) tracking (620) the current navigational position during said user'"'"'s navigation by viewing the user'"'"'s navigation as an n-order Markov chain accounting for a current page and recent viewing history,

e) utilizing the n-order Markov chain, calculating for said current position and a plurality of target pages a probability that the user will navigate to a respective one of said target pages based upon a number of times the respective one of the target pages has been visited after having traversed a determinate path to the respective one of the target pages, wherein said target pages are obtained by said utility ranking,

f) calculating (670) an expected saving factor for each target page based on said target page utility and on the navigational distance between said current page and said target page,

g) defining (675) at least one shortcut including the respective direct link to at least one of the target pages having a high expected saving factor,

h) displaying (680) said short cut as a clickable link to said user, and,

i) tracking which are the most followed paths for the user, and placing recommendations depending on where the user currently navigates, in order to reduce a number of clicks needed to navigate through the most popular navigation paths.

10. The computer program product of claim 9, wherein the n-order Markov model is a first order Markov model.