Category-based content recommendation

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.

I/We claim: 
     
         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 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 respectively with a first determined entity and a second determined entity that are part of the graph such that the corresponding category relates the first and second determined entities in an is-a relation, a part-of relation, or a member-of relation; 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 processing the corpus of content items includes ranking, for one of the content items, the determined multiple corresponding entities. 
     
     
         4 . The method of  claim 3  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. 
     
     
         5 . 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. 
     
     
         6 . 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. 
     
     
         7 . 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. 
     
     
         8 . 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.   
     
     
         9 . The method of  claim 8  wherein selecting the one or more content items includes ranking content items based on term frequency minus inverse document frequency. 
     
     
         10 . The method of  claim 8  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. 
     
     
         11 . The method of  claim 8  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. 
     
     
         12 . The method of  claim 8  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. 
     
     
         13 . The method of  claim 8  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. 
     
     
         14 . The method of  claim 8 , further comprising:
 determining content items that are related to the indicated category but that are not in the corpus of content items; and   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.   
     
     
         15 . The method of  claim 14  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. 
     
     
         16 . 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.   
     
     
         17 . A 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 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 respectively with a first determined entity and a second determined entity that are part of the graph such that the corresponding category relates the first and second determined entities in an is-a relation, a part-of relation, or a member-of relation; and   providing category-based content recommendations, by:
 receiving an indication of a category; 
 selecting a content item that has a corresponding category that matches the indicated category, the corresponding category associated with one or more entities that are referenced by the selected content item; and 
 transmitting an indication of the selected content item. 
   
     
     
         18 . The computer-readable medium of  claim 17  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. 
     
     
         19 . 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 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 respectively with a first determined entity and a second determined entity that are part of the graph such that the corresponding category relates the first and second determined entities in an is-a relation, a part-of relation, or a member-of relation; 
 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; and 
 transmit an indication of the selected content item. 
   
     
     
         20 . The computing system of  claim 19  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.