Abstract: A map generator machine generates influence maps based on profiles of entities, such as members of an online social networking service. The entities can be treated as nodes within a social graph, and each node may be represented by a corresponding node profile. The machine is configured to access a database of node profiles and rank the nodes according to seniority information contained in the node profiles. The machine is further configured to group nodes into clusters based on skill similarity based on skill descriptors included in their corresponding node profiles. The machine is also configured to generate one or more maps to depict one or more the subsets of the nodes. As generated, such a map is a graphical presentation of at least some of the nodes of the social graph, and the map may be generated with visual indicators of seniority and skill similarity.

Abstract: Techniques for searching for future candidates are disclosed herein. In some example embodiments, a future candidate system determines one or more target candidate attributes based on user input, and identifies one or more precedent candidate attributes based on a sequential relationship between the one or more precedent candidate attributes and the one or more target candidate attributes, with the one or more precedent candidate attributes being different from and preceding the one or more target candidate attributes. In some example embodiments, the future candidate system identifies one or more candidates from among a plurality of candidates based on a determination that the one or more candidates comprise the one or more precedent candidate attributes, and causes the identified one or more candidates to be displayed on a client device.

Abstract: A map generator machine generates influence maps based on profiles of entities, such as members of an online social networking service. The entities can be treated as nodes within a social graph, and each node may be represented by a corresponding node profile. The machine is configured to access a database of node profiles and rank the nodes according to seniority information contained in the node profiles. The machine is further configured to group nodes into clusters based on skill similarity based on skill descriptors included in their corresponding node profiles. The machine is also configured to generate one or more maps to depict one or more the subsets of the nodes. As generated, such a map is a graphical presentation of at least some of the nodes of the social graph, and the map may be generated with visual indicators of seniority and skill similarity.

Abstract: A search request is received at an information retrieval system. A repository of objects are searched for relevant objects based upon the search request. Parties are identified as parties who interacted with or are mentioned within the relevant objects. A relation score is determined for each pair of parties. A map is created based upon the relation scores of all the pairs of parties. The map is displayed such that the relation scores of the pairs of parties are shown. The determination of the relation score can be weighted by each relevant object's relevancy score. The relation score can also be dependent upon the each party's interactions with or mentions within the relevant objects, and the time lapse between one party's the interaction with or mention within a relevant object, and that of another party.

Abstract: A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.

Abstract: A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.

Abstract: A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.

Abstract: A search request is received from a requester including one or more search terms. In response to the search request, one or more objects are located that fulfill the search request. A relevancy score is computed for the one or more objects based on whether the object(s) include structured or unstructured data. The relevancy scores enable the requestor to determine the content relevancy of one or more objects.

Abstract: A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.

Abstract: A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.