RECOMMENDER SYSTEM FOR ON-LINE ARTICLES AND DOCUMENTS

• US 20090300547A1
• Filed: 01/27/2009
• Published: 12/03/2009
• Est. Priority Date: 05/30/2008
• Status: Abandoned Application

• Current Assignee: 8191808 Canada, Inc.
• Original Assignee: 8191808 Canada, Inc.
• Inventors: Wang, Biao, Paas, Julian, Xu, Bo, Yousefi, Pendar, Bates, Keith M.
• Application Number:
  US12/360,516
• Publication Number:
  US 20090300547A1
• Time in Patent Office: Days
• US Class Current: 715/825
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing

Abstract

A system and method for recommending on-line articles and documents to users is disclosed. The method provides an article widget user interface and a full-screen widget user interfaces to allow a user to rate articles, to preview articles, to filter articles based on category, article length, or other characteristics. A recommender system is configured to provide a continually refreshing list of recommended articles to the user via the user interfaces. The system comprises a module configured to monitor the user'"'"'s explicit and implicit interactions with the user interfaces, and provides a refreshed list of recommended articles accordingly. The recommender system may be configured to use a package of approaches including rule-based, content-based or collaborative filtering approaches including Slope, Co-Visitation, Mwinnow and Clustering/Co-clustering.

Claims

1. A computer-implemented method of providing recommendations for articles, comprising the steps:

(a) providing a user with an initial list of articles by displaying the initial list on a computer display device;

(b) receiving input from the user by receiving or monitoring input from at least one input device, said input comprising one or more of;

(i) an explicit rating for one of said initial list of articles;

(ii) user data in relation to the user;

(iii) an indication the user has changed or set a filter;

(c) generating in a microprocessor at least one new recommended article from a list of possible articles, based on the input received from the user;

(d) refreshing the initial list of articles with said at least one new recommended article to produce a refreshed list; and

,

(e) providing the user with the refreshed list by displaying the refreshed list on the computer display device.

2. The computer-implemented method of claim 1, where the user data comprises one or more of:

(iv) an indication user has viewed or accessed one of said initial list of articles;

(v) an indication user has viewed or accessed a preview of one of said initial list of articles;

(vi) an indication user has printed or emailed one of said initial list of articles

(vii) an indication user has tagged an article;

(viii) an indication user has accessed a tag.

3. The computer-implemented method of claim 1, where the user data comprises an indication user has dismissed one of said initial list of recommended articles.

4. The computer-implemented method of claim 1, where at least one instance of the initial list of articles or the refreshed list of articles shows the title of said instance and an image or icon associated with said instance.

5. The computer-implemented method of claim 1, where the initial list of articles is a list of recommended articles.

6. The computer-implemented method of claim 1, where the user data is used to generate an implicit rating of an article, and the implicit rating is used to generate the at least one new recommended article.

7. The computer-implemented method of claim 1, where the at least one new recommended article is generated based on at least two of the following approaches:

co-clustering, a rule based approach, a content based approach, Slope One, Mwinnow and Co-visitation.

8. The computer-implemented method of claim 7, where the recommended article is generated based on Mwinnow and one or more other approaches.

9. The computer-implemented method of claim 1, where the method further comprises the steps of:

(a) storing a ranked list of next articles to be provided to the user;

(b) refreshing the list with a top-ranked article, to generate a further refreshed list, when an item on the initial list or the updated list has been dismissed by the user;

(c) presenting the further refreshed list to the user.

10. The computer-implemented method of claim 1, where the method further comprises the steps of:

(a) storing a list of articles to be provided to the user;

(b) refreshing the list with an article from the stored list, to generate a further refreshed list, when an item on the initial list or the refreshed list has been dismissed by the user; and

,

(c) presenting the further refreshed list to the user.

11. The computer-implemented method of claim 1, where the method further comprises the steps of:

(a) labelling each instance of the possible list of articles as belonging to one or more groups;

(b) receiving input from the user indicating one or more desired groups;

(c) selecting the initial list of articles and the at least one new recommended article from instances of the list of possible articles which are a member of the one or more desired groups.

12. The computer-implemented method of claim 11, where the one or more groups include one or more of:

categories of articles, word length of articles, date of articles, number of images in the article, source of the article or author of the article.

13. A computer-implemented method of recommending articles, comprising the steps of:

(a) storing a set of possible articles in a database;

(b) receiving information from, or relation to, a first user, by receiving or monitoring input from at least one input device, said information including at least one of;

(i) demographic data about the first user;

(ii) rating data about one of the set of possible articles from the first user;

(iii) user data in relation to the first user;

(iv) transaction data in relation to the first user

(v) information relating to content of an article of interest to the first user.

(c) determining in a microprocessor a similarity between the received information and at least one of;

(i) demographic data about a second user;

(ii) rating data about one of the set of possible articles from the second user;

(iii) user data in relation to the second user;

(iv) transaction data in relation to the second user

(v) information relating to content of an article of interest to the second user.

(d) recommending to the first user information about a second article from the set of possible articles based on the determined similarity, by displaying the information about the second article on a computer display device, where the recommendation is generated by MWinnow.

14. The method of claim 13, where the recommendation is generated by MWinnow, and one or more of:

co-clustering, Slope One, Co-Visitation, content-based approach or a rules-based approach.

15. The method of claim 14 further comprising the steps of:

calculating a weighted average of results produced by MWinnow, Slope One, and Co-Visitation, and providing a recommendation when the weighted average exceeds a threshold.

16. A computer program product comprising:

a memory having computer readable code embodied therein, for execution by a CPU for recommending documents, said code comprising;

(a) code means for providing a user with an initial list of articles by displaying the initial list on a computer display device;

(b) code means for receiving input from the user by receiving or monitoring input from at least one input device, said input comprising one or more of;

(i) an explicit rating for one of said initial list of articles;

(ii) user data in relation to the user;

(iii) an indication user has changed or set a filter;

(c) code means for generating in a microprocessor at least one new recommended article from a list of possible articles, based on the input received from the user;

(d) code means for refreshing the initial list of articles with said at least one new recommended article to produce a refreshed list; and

,

(e) code means for providing the user with the refreshed list by displaying the refreshed list on the computer display device.

17. A computer program product comprising:

a memory having computer readable code embodied therein, for execution by a CPU for recommending articles, said code comprising;

(a) code means for storing a set of possible articles in a database;

(b) code means for receiving information from, or relation to, a first user by receiving or monitoring input from at least one input device, said information including at least one of;

(i) demographic data about the first user;

(ii) rating data about one of the set of possible articles from the first user;

(iii) user data in relation to the first user;

(iv) transaction data in relation to the first user

(v) information relating to content of an article of interest to the first user.

(c) code means for determining in a microprocessor a similarity between the received information and at least one of;

(i) demographic data about a second user;

(ii) rating data about one of the set of possible articles from the second user;

(iii) user data in relation to the second user;

(iv) transaction data in relation to the second user;

(v) information relating to content of an article of interest to the second user;

(d) code means for recommending to the first user information about a second article from the set of possible articles based on the determined similarity, by displaying the information about the second article on a computer display device, where the recommendation is generated by MWinnow.

18. A computer system comprising the following elements:

(a) an interface for receiving input from a user by receiving or monitoring input from at least one input device, said input comprising one or more of;

(i) an explicit rating for one of an initial list of articles presented to the user;

(ii) user data in relation to the user;

(iii) an indication the user has changed or set a filter;

(b) a user data collection module, for collecting the input from the user and for transmitting information to the user regarding articles;

(c) a database, for storing the input, a list of possible article ratings table, and article and user table;

(d) a recommender module, for recommending to the user information about one of the list of possible documents, said recommendation based on said input.

19. The computer system claimed in claim 18, further comprising:

a user data pre-processing module, which performs one or more of;

generating an implicit rating for an article.

20. The computer system claimed in claim 18, further comprising a user data analysis module for carrying out one or more of the following:

clustering, co-clustering, pattern analysis.