Abstract: Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.

Abstract: The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

Abstract: The present disclosure relates to systems and methods for automatically associating additional content with sports games. The systems and methods obtain game schedule data for a plurality of sports games, and for each scheduled game, the systems and methods select articles published within a timeframe of the game date of the scheduled game. The systems and methods identify sports articles associated with the sports game based on entities and event phrases extracted from the sports articles matching the schedule game data. The systems and methods classify the sports articles into a plurality of clusters and use the clusters to associate the sports articles to the game schedule data and store the associated sports articles with the game schedule data.

Abstract: The present disclosure relates to systems, devices, and methods for identifying structured data for any webpage when a user requests the webpage, or the webpage loads in a browser. The systems, devices, and methods extract a portion of the webpage content and use the webpage content to determine the domain of the webpage, extract entities from the webpage content, query one or more datastores with content for structured data based on the domain and the extracted entities and present the structured data with the webpage.

Abstract: The present disclosure relates to systems, devices, and methods for identifying additional content for an article. The systems, devices, and methods may identify a domain for the articles and content and may use machine learning models to classify the articles and the content into categories using smart tags for the domain. The systems, devices, and methods may convert the articles and the content into document vectors using a pre-trained domain specific language model and generate a relevance score for the articles and the content using the document vectors. The systems, devices, and methods may generate a list of predicted matches that includes content that is similar to the article based on the relevance score. The systems, devices, and methods may filter the list of predicted matches based on a temporal proximity to generate a list of additional content for the article.

Abstract: A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

Abstract: A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

Abstract: Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.

Abstract: Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Abstract: Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Abstract: A search engine query can be received, along with contextual data encoding information about a context of the query. The query can be classified into a selected user interface profile of multiple available user interface profiles, with the classifying including applying a classification model to the contextual data. A visual structure generator can be selected using results of the classifying, and a search results page can be generated for the query. The generating of the search results page can include using the selected visual structure generator to impose a selected visual structure on the search results page, with the selected visual structure corresponding to the selected visual structure generator. The generated search results page can be returned in response to the receiving of the query.

Abstract: Mechanisms to personalize quick answers to a user are described. A query history is assembled from queries previously submitted by users. Each entry in the query history comprises a query, an associated quick answer, and the userID associated with the user that submitted the query. A query database is created by submitting the query of each query entry in the query history to a trained machine learning classifier to classify the query with an associated quick answer segment. The quick answer segment is combined with the other information in the query history to create an entry into a query database. When a current query is received from a user, the query database is searched and prior queries with the same quick answer segment from the same user are extracted. A subset of the resultant queries are combined with a subset of queries from other users to personalize the search results.

Abstract: Methods and systems are disclosed for post-ranking a ranked search result based on a personal affinity of a user. Issues on ranking a search result of information based on user-level optimization without breaking ranking of the search results based on global optimization functions are addressed by first ranking a search result based on the global optimization functions, followed by post-ranking the ranked search result based on a personal affinity of the user. The personal affinity may be determined based on a search history by the user as captured in a knowledge base. The post-ranking is performed on a limited scope by dividing the ranked search result into multiple portions and re-ranking entries within respective portions based on the personal affinity, for example, by boosting entries that matches the personal affinity to the top of the entries within the portion.

Abstract: A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

Abstract: The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

Abstract: Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Abstract: A search engine query can be received, along with contextual data encoding information about a context of the query. The query can be classified into a selected user interface profile of multiple available user interface profiles, with the classifying including applying a classification model to the contextual data. A visual structure generator can be selected using results of the classifying, and a search results page can be generated for the query. The generating of the search results page can include using the selected visual structure generator to impose a selected visual structure on the search results page, with the selected visual structure corresponding to the selected visual structure generator. The generated search results page can be returned in response to the receiving of the query.