Category-based content recommendation
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.
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; and
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,
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.
2. 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; and
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,
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.
3. 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; and
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,
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.
4. 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; and
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.