BI-MODEL RECOMMENDATION ENGINE FOR RECOMMENDING ITEMS AND PEERS

• US 20130159325A1
• Filed: 06/20/2012
• Published: 06/20/2013
• Est. Priority Date: 02/03/2010
• Status: Active Grant

• Current Assignee: Gartner, Inc.
• Original Assignee: Gartner, Inc.
• Inventors: Polonsky, Nir, Malangi, Girish, Keif, Brian, Asumu, Donatus, Carroll, Chris, Vittal, Malini
• Granted Patent: US 8,661,034 B2
• Time in Patent Office: Days
• US Class Current: 707/754
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536

Abstract

A networked peer and item recommendation system makes recommendations to users such as documents of interest and peers with whom the users may want to connect. User profile information is maintained in a profiles database. A log enables the collection of user behavior information. A cluster filtering algorithm determines a cluster that a querying user belongs to. A collaborative filtering algorithm locates other users having implicit and explicit profiles in the database that are similar to the profile of the querying user. A dynamic expert verification algorithm is associated with the collaborative filtering algorithm to verify the qualifications of users and produce bias towards users with higher quality interactions. A search engine returns items based on a keyword provided by the querying user. A sorting algorithm sorts the items returned by the cluster filtering algorithm, collaborative filtering algorithm and search engine for presentation to the querying user.

Claims

1. A system for verifying a user as an expert relative to a querying user, the system being implemented on a digital computer network, the system comprising:

a computer coupled to the network;

a profiles database stored in memory associated with the computer;

a user interface coupled to the computer and enabling user profile information to be entered and stored in the profiles database;

a collaborative filtering algorithm associated with the profiles database, said collaborative filtering algorithm;

(a) locating other users having profiles in the database based on a similarity among the profiles of a querying user and the other users based on at least one of explicit and implicit profiles,

(b) locating other users based on their expertise to define verified experts for the keyword provided by the querying user and prioritizing the verified experts with most expertise for the keyword,

(c) determining scores of the other users located in steps (a) and (b) indicative of how well the other users match the querying user,

(d) locating items used by a best matching subset of the other users located in steps (a) and (b) based on the scores, and

(e) returning the items located in step (d) for consideration by the querying user; and

a dynamic expert verification algorithm associated with the collaborative filtering algorithm, the expert verification algorithm being adapted to verify qualifications of the other users as experts relative to the querying user, the expert verification algorithm comprising logic used to objectively score at least one of a depth and a frequency of the user'"'"'s activity related to a topic area.

2. The system in accordance with claim 1, wherein the logic objectively scores both the depth and frequency of the user'"'"'s activity.

3. The system in accordance with claim 2, wherein the dynamic expert verification algorithm comprises a database in which peer to peer and peer to expert analyst interactions are recorded.

4. The system in accordance with claim 3, wherein the logic used to objectively score the depth and the frequency of a peer is based on the quality of the peer'"'"'s interactions with other peers and expert analysts.

5. The system in accordance with claim 4, wherein a measurement of the interaction between the users is scored based on a scheduled interaction of the querying user with expert analysts.

6. A method for verifying a user as an expert relative to a querying user, the method being implemented on a digital computer network, the method comprising:

providing a computer coupled to the network;

providing a profiles database stored in memory associated with the computer;

providing a user interface coupled to the computer and enabling user profile information to be entered and stored in a profiles database;

locating other users in said database that are similar to the profile of a querying user based on at least one of explicit and implicit profiles;

locating other users in said database that have the most expertise for a keyword provided by the querying user;

determining scores of the other users located indicative of how well said other users match said querying user;

based on said scores, locating items used by a best matching subset of the other users located;

returning the items located on the basis of the best matching subset for consideration by the querying user; and

verifying the qualifications of the users as experts, utilizing logic to objectively score at least one of a depth and a frequency of human interactions with peers and expert analysts related to a topic area.

7. The method in accordance with claim 6, wherein the logic is utilized to objectively score both the depth and frequency of said human interactions.

8. The method in accordance with claim 7, wherein the step of verifying the qualifications of the other users comprises logging peer to peer interactions and peer to expert analysts interactions into a database.

9. The method in accordance with claim 8, wherein the step of verifying the qualifications by utilizing logic to objectively score the depth and the frequency of the user'"'"'s activity related to the topic area is based on values assigned to the user'"'"'s interactions with other peers and expert analysts.

10. The method in accordance with claim 9, wherein the step of verifying the qualifications by utilizing logic to objectively score the depth and the frequency of the user'"'"'s activity produces a positive bias towards users with higher quality interactions with their peers and expert analysts in a given subject matter.

11. The method in accordance with claim 10, wherein a preference in the scoring is given to newer interactions as compared to older interactions.

12. The method in accordance with claim 8, wherein the step of logging interactions between the peers and expert analysts and the querying user into a database comprises logging subject matter of interactions between the peers and expert analysts and the querying user into a database.

13. The method in accordance with claim 12, further comprising reviewing at least one of the peers and expert analysts and the querying user for a quality of human interaction.

14. The method in accordance with claim 13, further comprising assessing and qualifying at least one of the peers and expert analysts and the querying user with regard to the quality of human interaction.

15. The method in accordance with claim 14, further comprising rating the quality of the human interaction.

16. The method in accordance with claim 15, further comprising filtering the rated human interactions to retain users with values exceeding a predetermined level.