METHODS AND SYSTEMS FOR IMPROVING ENGAGEMENT WITH A RECOMMENDATION ENGINE THAT RECOMMENDS ITEMS, PEERS, AND SERVICES

• US 20140257890A1
• Filed: 02/27/2014
• Published: 09/11/2014
• Est. Priority Date: 03/05/2013
• Status: Active Grant

• Current Assignee: Gartner, Inc.
• Original Assignee: Gartner, Inc.
• Inventors: Polonsky, Nir, Rosenblatt, Andrew, Malangi, Girish, Davis, Kendall Bruce, Cho, Richard N.
• Granted Patent: US 10,600,011 B2
• Time in Patent Office: Days
• US Class Current: 705/7.11
• CPC Class Codes: G06F 15/16 Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs coordinating program control therefor G06F9/52; in regulating and control system G05B
• CPC Class Codes: G06F 3/01 Input arrangements or combined input and output arrangements for interaction between user and computer G06F3/16 takes precedence
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Computerized methods and systems for improving engagement with a recommendation engine that recommends items, peers, and services are provided. Stored data of a plurality of users is electronically accessed and analyzed. A respective passive profile is determined for each of the users based on the analyzing of the stored data. The respective passive profiles are then stored for use by the recommendation engine. The recommendation engine can then provide recommendations for at least one of items, peers, and services to a respective user based on at least the respective passive profile. The recommendations may be further based on at least one of an explicit profile comprising information provided by the respective user and an activity profile based on tracked activity of the respective user with regard to prior recommended items.

Claims

1. A computerized method for improving engagement with a recommendation engine that recommends items, peers, and services, comprising:

electronically accessing stored data of a plurality of users;

analyzing the stored data of each of the users;

determining a respective passive profile for each of the users based on the analyzing of the stored data;

storing the respective passive profiles for use by the recommendation engine; and

providing recommendations for at least one of items, peers, and services to a respective user based on at least the respective passive profile.

2. The method in accordance with claim 1, wherein:

the recommendations are further based on at least one of an explicit profile comprising information provided by the respective user and an activity profile based on tracked activity of the respective user;

the tracked activity of the user relates to at least one of recommended items reviewed, keyword searches entered, peers interacted with, items purchased, online discussions participated in, documents opened, items downloaded, and virtual events attended.

3. The method in accordance with claim 1, wherein the recommendations are further based on one or more of the passive profiles of other of the users which are similar to the respective passive profile.

4. The method in accordance with claim 1, further comprising:

tracking at least one of the respective user'"'"'s interaction with the user'"'"'s stored data and changes made to the stored data; and

maintaining a passive interaction profile for each respective user based on said tracking.

5. The method in accordance with claim 4, further comprising at least one of:

periodically updating the respective passive interaction profiles for the users based on the tracking; and

using the passive interaction profiles to improve the recommendations.

6. The method in accordance with claim 4, further comprising:

providing recommendations for at least one of experts, documents, articles, news items, announcements, charts, graphics, reports, web pages, events to attend, keyword searches, software updates, additional software programs, additional hardware, software information items, hardware information items, or appropriate services based on at least one of the respective passive profile and said passive interaction profile.

7. The method in accordance with claim 4, further comprising:

providing a mapping tool for mapping information obtained from said stored data to the respective passive profiles and for mapping information obtained from said tracking to the respective passive interaction profiles; and

filtering the information contained in the passive profiles and the passive interaction profiles for use in the providing of the recommendations.

8. The method in accordance with claim 7, further comprising:

applying a cluster filter to the passive profiles and the passive interaction profiles for;

identifying users having similar passive profiles and passive interaction profiles;

grouping the identified users with similar passive profiles and passive interaction profiles into clusters and storing the clusters in a cluster database;

locating at least one of the items, peers, or services associated with a keyword provided by a respective user and also used by other users in the same cluster as the respective user; and

returning at least one of the items, peers, or services to the respective user.

9. The method in accordance with claim 8, further comprising:

applying a collaborative filter to at least one of the passive interaction profile and the cluster database for;

identifying other users having similar passive profiles and passive interaction profiles;

grouping the identified users with similar passive profiles and passive interaction profiles into peer groups and storing the peer groups for each respective user in a peer database;

scoring each of the identified other users based on how well the passive profile and the passive interaction profile of the respective user matches the passive profiles and the passive interaction profiles of the other users;

locating at least one of items, peers, or services used by a best-matching subset of the indentified users based on said scoring; and

returning at least one of the items, peers, or services to the respective user.

10. The method in accordance with claim 9, wherein:

a search engine is used to return the at least one of items, peers, and services to the respective user based on a keyword search of the respective user; and

the search engine locates the at least one of items, peers, and services from the other users that are not in the respective user'"'"'s cluster or peer group.

11. The method in accordance with claim 10, further comprising:

applying a sorting algorithm for sorting the at least one of the items, peers, and services returned by the cluster filter, the collaborative filter, and the search engine;

scoring the at least one of the items, peers, and services by weights assigned to each of the cluster filter, the collaborative filter, and the search engine;

wherein the at least one of the items, peers, and services returned by the cluster filter and the collaborative filter are given precedent to the at least one of the items, peers, and services returned by the search engine.

12. The method in accordance with claim 9, further comprising:

dynamically qualifying other users from the respective user'"'"'s peer group as experts.

13. The method in accordance with claim 12, wherein:

the other users are qualified as experts by;

identifying overlaps between at least one of the passive profiles, passive interaction profiles, and peer interactions of the respective user and the other users in the peer group;

scoring the overlaps for each of the other users based on an extent of the respective overlaps; and

biasing the recommendations of experts based on the scoring.

14. The method in accordance with claim 9, further comprising:

applying a peer searching algorithm for identifying other users as peers that at least one of;

have keywords in their respective passive profiles that are the same as or are related to the keyword search;

have explicit profiles comprising information provided by the other users that are related to the keyword search; and

have passive interaction profiles that are related to the keyword search;

sorting the peers identified by the peer search algorithm and peers identified by the collaborative filtering algorithm, giving precedence to the peers identified by the collaborative filtering algorithm over the peers identified by the peer search algorithm.

15. The method in accordance with claim 4, further comprising:

enabling alerts to be provided to the recommendation engine when changes to one of the passive profiles or the passive interaction profiles are detected;

automatically constructing a query for the respective user with keywords based on the detected change;

providing at least one of recommended items, peers, and services returned by the query to the respective user.

16. The method in accordance with claim 15, further comprising:

notifying other users having similar passive profiles to the passive profiles with the detected changes of the respective detected changes and the recommended items, peers, and services returned by the query.

17. The method in accordance with claim 16, wherein the notifying includes providing the identity of the user having the passive profile with the detected changes.

18. The method in accordance with claim 4, further comprising:

aggregating at least one of all passive profiles, all passive interaction profiles, and all detected changes to provide benchmarks for industry standards; and

providing users with a comparison between at least one of (i) at least one element of their respective passive profiles and the relevant benchmark; and

(ii) at least one element of their respective passive interaction profiles and the relevant benchmark.

19. The method in accordance with claim 1, wherein:

the analyzing of the user'"'"'s stored data occurs with a predetermined frequency in order to update each of the passive profiles.

20. The method in accordance with claim 1, wherein the stored data relates to at least one of inventory lists, data lists, business documentation, software applications, computer hardware data, electronic device data, entertainment inventory comprising at least one of songs, movies, and electronic books, travel data, household data, personal data, grocery lists, utility data, sports equipment listings, hobby data, automobile records, personal interests data, performance data pertaining to a machine or a device.

21. A computerized system for improving engagement with a recommendation engine that recommends items, peers, and services, comprising:

a network connecting a recommendation engine to a plurality of user devices;

a software application for electronically accessing and analyzing data stored on the user devices and determining a respective passive profile for each of the users based on the analyzing of the stored data;

a database for storing the respective passive profiles for use by the recommendation engine; and

wherein the recommendation engine provides recommendations for at least one of items, peers, and services to the respective users based on at least the respective passive profile.