Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.

Abstract: Methods and systems for integrating images with the associated text-based content signals and data about users' preferences to determine an image or user intent. Methods and implementations for monetizing these images is also described.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: A solution for identifying related concepts of URLs and domain names includes using structural parsing to extract information from user input comprising a URL or domain name. The information includes one or more of a protocol, a location, and a subdirectory. Semantic parsing of the information is used to identify a first one or more concepts represented by one or more tokens within the extracted information. A content association map is queried to retrieve a second one or more concepts related to the first one or more concepts. Each of the concepts represents a unit of thought, expressed by a term, letter, or symbol. The concept association map includes a representation of concepts, concept metadata, and relationships between the concepts. The first one or more concepts and the second one or more concepts are ranked, and the ranked concepts are stored for displaying to one or more users of the computer platform.

Abstract: A solution for identifying related concepts of URLs and domain names includes using structural parsing to extract information from user input comprising a URL or domain name. The information includes one or more of a protocol, a location, and a subdirectory. Semantic parsing of the information is used to identify a first one or more concepts represented by one or more tokens within the extracted information. A content association map is queried to retrieve a second one or more concepts related to the first one or more concepts. Each of the concepts represents a unit of thought, expressed by a term, letter, or symbol. The concept association map includes a representation of concepts, concept metadata, and relationships between the concepts. The first one or more concepts and the second one or more concepts are ranked, and the ranked concepts are stored for displaying to one or more users of the computer platform.

Abstract: A pre-computed concept map represents concepts, concept metadata, and relationships between the plurality of concepts. Online user behavior may be predicted by correlating one or more online events of a user with one or more features of the concept map, aggregating a concept map history of the user to obtain online behavior over time, aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior, and predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior. The predicted behavior may be used to target ads that the user is likely to find relevant.

Abstract: A method and apparatus for optimizing long-term revenues in online auctions of distinguishable units of an item. For example, the item might be a specific keyword in sponsored search where the distinguishable units could be different slots for putting ads. The system can estimate parameters such as relevance and value by using a notion of fairness and can optimize the revenue by effecting the users' incentives and by improving the bidding language. In general, the method and the system can also be used for equivalent offline auctions.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.