Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

Abstract: A server system associates one or more locations with a query by identifying the query, selecting a set of documents responsive to the query, and assigning weights to respective documents in the set of documents based, at least in part, on historical data of user clicks selecting search result links in search results produced for historical queries substantially the same as the identified query. Websites hosting the selected documents are identified, and, for each website, location-specific information for one or more locations is retrieved, including a location-specific score that corresponds to the likelihood that the respective location corresponds to a respective website. For each respective location for which location-specific information was retrieved, aggregating the location-specific scores, as weighted by the document weights, to compute an aggregated likelihood that the respective location is associated with the query.

Abstract: A server system associates one or more locations with a query by identifying the query, selecting a set of documents responsive to the query, and assigning weights to respective documents in the set of documents based, at least in part, on historical data of user clicks selecting search result links in search results produced for historical queries substantially the same as the identified query. Websites hosting the selected documents are identified, and, for each website, location-specific information for one or more locations is retrieved, including a location-specific score that corresponds to the likelihood that the respective location corresponds to a respective website. For each respective location for which location-specific information was retrieved, aggregating the location-specific scores, as weighted by the document weights, to compute an aggregated likelihood that the respective location is associated with the query.

Abstract: A server system associates one or more locations with a query by identifying the query, selecting a set of documents responsive to the query, and assigning weights to respective documents in the set of documents based, at least in part, on historical data of user clicks selecting search result links in search results produced for historical queries substantially the same as the identified query. Websites hosting the selected documents are identified, and, for each website, location-specific information for one or more locations is retrieved, including a location-specific score that corresponds to the likelihood that the respective location corresponds to a respective website. For each respective location for which location-specific information was retrieved, aggregating the location-specific scores, as weighted by the document weights, to compute an aggregated likelihood that the respective location is associated with the query.