Category-based content recommendation

• US 8,725,739 B2
• Filed: 11/01/2011
• Issued: 05/13/2014
• Est. Priority Date: 11/01/2010
• Status: Active Grant

• Current Assignee: Fiver LLC
• Original Assignee: EVRI Incorporated
• Inventors: Diamond, Ted, Cooper, Jennifer, Koperski, Krzysztof, Liang, Jisheng
• Primary Examiner(s): GIRMA, ANTENEH B
• Application Number:
  US13/286,778
• Publication Number:
  US 20120109966A1
• Time in Patent Office: 924 Days
• Field of Search: None
• US Class Current: 707/740
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing

Abstract

Techniques for category-based content recommendation are described. Some embodiments provide a content recommendation system (“CRS”) configured to recommend content items (e.g., Web pages, images, videos) that are related to specified categories. In one embodiment, the CRS processes content items to determine entities referenced by the content items, and to determine categories related to the referenced entities. The determined entities and/or categories may be part of a taxonomy that is stored by the CRS. Then, in response to a received request that indicates a category, the CRS determines and provides indications of one or more content items that each have a corresponding category that matches the indicated category. In some embodiments, at least some of these techniques are employed to implement a category-based news service.

Claims

1. A computer-implemented method in a content recommendation system, the method comprising:

processing a corpus of content items to determine, for each of the content items, multiple corresponding entities referenced by the content item, each of the determined entities being electronically represented by the content recommendation system;

determining, for each of at least some of the content items, at least one corresponding category that is part of a taxonomy that is that is represented as a graph stored by the content recommendation system and that is associated with one of the multiple corresponding entities referenced by the content item, wherein determining the at least one corresponding category includes aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by;

determining a first taxonomic path associated with a first one of the determined entities, the first path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity;

determining a second taxonomic path associated with a second one of the determined entities, the second path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the second entity; and

determining a common node between the first and second taxonomic paths, the common node representing the at least one corresponding category, such that the firs and second entities are both in an is-a relationship with the at least one corresponding category; and

storing, for each of the content items, the determined multiple corresponding entities and the determined at least one corresponding category.

2. The method of claim 1 wherein determining the at least one corresponding category includes traversing a path in the taxonomy stored by the content recommendation system.

3. The method of claim 1 wherein determining the at least one corresponding category includes traversing one or more relations in the taxonomy stored by the content recommendation system, the relations including at least one of:

an is-a relation, a part-of relation, or a member-of relation.

4. The method of claim 1 wherein processing the corpus of content items includes ranking, for one of the content items, the determined multiple corresponding entities.

5. The method of claim 4 wherein ranking the determined multiple corresponding entities includes ranking an entity based on at least one of:

the quantity of times that the entity is referenced by the content item, a position/location of a reference of the entity, or a penalty assessed based on a type of the entity.

6. The method of claim 4 wherein determining the at least one corresponding category includes selecting a predetermined number of highest ranked entities from the ranked entities.

7. The method of claim 1 wherein determining the at least one corresponding category includes ranking leaf node categories of taxonomic paths associated with the determined multiple corresponding entities, the ranking based on the quantity of entities having a particular category and/or the a rank of an entity in a ranked list of entities.

8. The method of claim 1 wherein storing the determined multiple corresponding entities and the determined at least one corresponding category includes annotating a content item entry in an index with tokens that reflect the determined at least one category.

9. The method of claim 8, further comprising:

searching for content items in the index that include a specified token that reflects the indicated category.

10. The method of claim 1, further comprising:

receiving an indication of a category;

selecting one or more of the content items that each have a corresponding category that matches the indicated category; and

providing indications of the selected content items.

11. The method of claim 10 wherein selecting the one or more content items includes ranking content items based on term frequency minus inverse document frequency.

12. The method of claim 10 wherein selecting the one or more content items includes ranking content items based the quantity of times a category token was added to a content item index.

13. The method of claim 10 wherein selecting the one or more content items includes ranking the one or more content items based on a credibility score determined for each content item.

14. The method of claim 10 wherein selecting the one or more content items includes ranking the one or more content items based on recency of each content item, such that more recent content items are ranked higher than less recent content items.

15. The method of claim 10 wherein selecting the one or more content items includes collapsing similar content items into groups of content items, wherein similarity between two content items is based on at least one of:

distance between signatures of the two content items, amount of overlap between titles of the two content items, amount of overlap between summaries of the two content items, amount of overlap between URLs referencing the two content items, and publishers of the two content items.

16. The method of claim 10, further comprising:

determining content items that are related to the indicated category but that are not in the corpus of content items.

17. The method of claim 16 wherein determining content items that are related to the indicated category but that are not in the corpus of content items includes generating a keyword query that includes names of popular entities in the indicated category.

18. The method of claim 16 wherein determining content items that are related to the indicated category but that are not in the corpus of content items includes receiving from a third-party content service indications of at least one of a video, an image, an audio file, an instant message, or a message in a social network.

19. The method of claim 10 wherein providing indications of the selected content items includes presenting information about the selected content items on a display screen.

20. The method of claim 10 wherein providing indications of the selected content items includes transmitting information about the selected content items to a remote client system.

21. The method of claim 1, further comprising:

determining popular entities for an indicated category, the popular entities having recently received an increased number of references by content items in the corpus and/or having more references by content items in the corpus than other entities; and

transmitting indications of the determined popular entities.

22. A non-transitory computer-readable medium having contents that, when executed, enable a computing system to recommend content, by performing a method comprising:

processing a corpus of content items to determine, for each of the content items, multiple corresponding entities referenced by the content item, each of the determined entities being electronically represented by the content recommendation system;

determining, for each of at least some of the content items, at least one corresponding category that is part of a taxonomy that is represented as a graph stored by the content recommendation system and that is associated with one of the multiple corresponding entities referenced by the content item, wherein determining the at least one corresponding category includes aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by;

determining a first taxonomic path associated with a first one of the determined entities, the first path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity;

determining a second taxonomic path associated with a second one of the determined entities, the second path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the second entity; and

determining a common node between the first and second taxonomic paths, the common node representing the at least one corresponding category, such that the first and second entities are both in an is-a relationship with the at least one corresponding category;

providing category-based content recommendations, by;

receiving an indication of a category;

selecting a content item that is one of the at least some of the content items and that has a corresponding category that matches the indicated category, the corresponding category being one of the determined categories; and

transmitting an indication of the selected content item.

23. The computer-readable medium of claim 22 wherein selecting the content item includes collapsing similar content items into groups of content items, wherein similarity between two content items is based on at least one of:

distance between signatures of the two content items, amount of overlap between titles of the two content items, amount of overlap between summaries of the two content items, amount of overlap between URLs referencing the two content items, and/or publishers of the two content items.

24. The computer-readable medium of claim 22 wherein the computer-readable medium is a memory in the computing system.

25. The computer-readable medium of claim 22 wherein the contents are instructions that, when executed, cause the computing system to perform the method.

26. A computing system configured to recommend content, comprising:

a memory;

a module stored on the memory that is configured, when executed, to;

process a corpus of content items to determine, for each of the content items, multiple corresponding entities referenced by the content item, each of the determined entities being electronically represented by the content recommendation system;

determine, for each of at least some of the content items, at least one corresponding category that is part of a taxonomy that is represented as a graph stored by the content recommendation system and that is associated with one of the multiple corresponding entities referenced by the content item, wherein the at least one corresponding category is determined by aggregating common nodes in taxonomic paths that are associated with the determined entities and that are part of the graph, by;

determining a first taxonomic path associated with a first one of the determined entities, the first path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity;

determining a second taxonomic path associated with a second one of determined entities, the second path including multiple connected nodes that are in the graph and that represent a hierarchy of categories for the first entity;

determining a common node between the first and second taxonomic paths, the common node representing the at least one corresponding category, such that the first and second entities are both in an is-a relationship with the at least one corresponding category; and

receive from a search query an indication of a category;

select a content item from the corpus of content items, the selected content item having a corresponding category that matches the indicated category, the corresponding category being one of the determined categories; and

transmit an indication of the selected content item.

27. The computing system of claim 26 wherein the module includes software instructions for execution in the memory of the computing system.

28. The computing system of claim 26 wherein the module is a content recommendation system.

29. The computing system of claim 26 wherein the module is a category-based news service configured to recommend news items to at least one of a personal digital assistant, a smart phone, a tablet computer, a laptop computer, and/or a third-party application.