Online Game Having a Computerized Recommender System

• US 20160346687A1
• Filed: 05/25/2016
• Published: 12/01/2016
• Est. Priority Date: 05/26/2015
• Status: Abandoned Application

• Current Assignee: Jagex Limited (Shanghai Fukong Interactive Entertainment Co., Ltd.)
• Original Assignee: Jagex Limited (Shanghai Fukong Interactive Entertainment Co., Ltd.)
• Inventors: Kerrien, Samuel Claude, Worgan, Simon Forester
• Application Number:
  US15/164,843
• Publication Number:
  US 20160346687A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A63F 13/335 using Internet
• CPC Class Codes: A63F 13/35 Details of game servers
• CPC Class Codes: A63F 13/79 involving player-related data, e.g. identities, accounts, preferences or play histories
• CPC Class Codes: A63F 13/85 Providing additional services to players
• CPC Class Codes: A63F 2300/535 for monitoring, e.g. of user parameters, terminal parameters, application parameters, network parameters

Abstract

An online game system is described that is configured to provide at least one recommendation in an online game for a single user of a plurality of users of the online game by: ranking, based on desirability of at least some of the plurality of users'"'"' past behavior within the online game, mutually preferred sets of sequences of in-game activities previously carried out by at least some of the plurality of users of the online game and sets of sequences of in-game activities available at locations within the online game; and providing at least one recommendation to the single user based on the ranking.

Claims

1. An online game system configured to provide at least one recommendation in an online game for a single user of a plurality of users of the online game, comprising:

a computerized recommender system configured to rank, based on desirability of at least some of the plurality of users'"'"' past behavior within the online game, mutually preferred sets of sequences of in-game activities previously carried out by at least some of the plurality of users of the online game and sets of sequences of in-game activities available at locations within the online game;

wherein the online game system provides at least one recommendation of an in-game activity to a single user, based upon the desirability rank of that activity sequentially following prior activities of the single user, as ranked by the computerized recommender system.

2. An online game system according to claim 1, wherein the desirability of the plurality of users'"'"' past behavior comprises at least one of:

time in-game by the plurality of users and spend in-game by the plurality of users.

3. An online game system according to claim 1, wherein the at least one recommendation comprises at least one location for the user to go to in-game.

4. An online game system according to claim 1, wherein the at least one recommendation comprises at least one activity for the user to carry out in-game.

5. An online game system according to claim 1, wherein the sets of sequences of in-game activities comprise generalized sets of sequences of in-game activities.

6. An online game system according to claim 1, wherein each user has an associated set of user features, wherein the at least one recommendation comprises a set of recommended in-game locations, and wherein the system is configured to provide a ranking of sequences of in-game activities associated with each recommended location according to the mutual preference between the sequences of in-game activities associated with each recommended location and the set of user features for the single user, and to select a plurality of sequences of in-game activities according to the ranking to provide a set of location-activity sequence pairs for the single user.

7. An online game system for a plurality of users, comprising one or more game servers hosting an online game and a computerized recommender system connected to the one or more game servers for providing recommendations in the online game;

the computerized recommender system being configured to;

for each of a plurality of recommended actions, determine preceding user actions that lead to that recommended action to generate a set of user action/recommendation pairs;

produce a reduced set of user action/recommendation pairs from the set of user actions by removing user action/recommendation pairs having an equivalent action/recommendation pair within the set of user action/recommendation pairs; and

identify the most frequently occurring user action/recommendation pairs from those user action/recommendation pairs within the reduced set of user action/recommendation pairs to form a set of rules for providing recommendations within the online game;

the one or more game servers being configured to deliver recommendations to users of the online game based on the set of rules and on actions taken by the users within the online game.

8. An online game system according to claim 7, wherein the user actions comprise sets of user events, and wherein the computerized recommender system is configured to categorise user actions comprising the same user events in different order as equivalent user actions.

9. An online game system according to claim 7, wherein the computerized recommender system is further configured to:

rank the rules in the set of rules based on predicted impact of the recommendation in the online game;

select highest ranked rules; and

output the selected highest ranked rules as a set of generic rules for the online game.

10. An online game system according to claim 9, wherein the one or more game servers are configured to:

compare a single user'"'"'s actions with the set of generic rules and if the user'"'"'s actions correspond to one of the generic rules, provide a generic recommendation corresponding to that generic rule;

compare the generic recommendation with a ranked list of recommendations for the single user; and

deliver to the user the generic recommendation if it is also present in the ranked list of recommendations for the single user.

11. An online game system according to claim 7, wherein the recommender system is configured to export an updated user specific recommendations data set to the game server based on users'"'"' in-game activity, and wherein the game server is configured to switch from a current user specific recommendations data set to the updated user specific recommendations data set after the updated user specific recommendations data set has been exported to the game server.

12. An online game system according to claim 11, wherein the updated user specific recommendations data set differs from the current user specific recommendations data set only for those users that have used the online game since the export of the current user specific recommendations data set.

13. An online game system configured to providing recommendations in an online game to a single user of a plurality of users of the online game, wherein the game system is configured to:

compare a ranked list of recommendations for the single user with a generic recommendation taken from a list of generic recommendations for the plurality of users; and

deliver the generic recommendation if it is on the ranked list of recommendations for the single user.

14. An online game system according to claim 13, wherein the ranked list of recommendations for the single user is based on recent activity of the single user in the game.

15. An online game system according to claim 13, wherein the ranked list of recommendations for the single user is ranked based on predicted impact within the game.

16. An online game system according to claim 13, wherein the computerized recommender system is further configured to:

provide the generic recommendation based on the single user'"'"'s actions within the game.

17. An online game system according to claim 13, wherein the recommender system is configured to update the ranked list of recommendations for the single user if and only if the single user has used the online game since a most recent update.

18. An online game system according to claim 13, wherein the recommender system is configured to export an updated user specific recommendations data set to the game server based on users'"'"' in-game activity, and wherein the game server is configured to switch from a current user specific recommendations data set to the updated user specific recommendations data set after the updated user specific recommendations data set has been exported to the game server.

19. An online game system comprising a game server hosting an online game for a plurality of users and a computerized recommender system for providing recommendations to the online game, the recommender system comprising a high level recommender sub-system and a low level recommender sub-system, the high level recommender sub-system being configured to:

receive high level game activity data from the online game, wherein the high level game activity data is human readable data describing recently occurred in-game activity and is encoded low level game activity data;

generate high level recommendations based on the high level game activity data; and

export the high level recommendations to the online game;

the low level recommender sub-system being configured to receive low level game activity data from the online game, wherein the low level game activity data is low level data describing the recently occurred in-game activity; and

generate low level recommendations based on the low level game activity data;

the recommender system being configured to compare the low level recommendations from the low level recommender sub-system with the high level recommendations from the high level recommender sub-system, and if there is a low level recommendation that has no corresponding high level recommendation, output the low level recommendation that has no corresponding high level recommendation so that it can be encoded as high level activity data.

20. An online game system according to claim 19, wherein the high level game activity comprises user actions and wherein each user action comprises one or more low level variable changes.