Abstract: Systems and methods for improving search results using assessment based routing are provided. In example embodiments, social network data for members in an organization and members outside the organization is accessed. An internal member and external member may share one or more attributes, such as working on the same project or within the same company. An electronic assessment can be sent to the internal member to generate assessment data about the external member. The assessment data can be included in an electronic message sent to the external member. Interaction data from the electronic message can be received when the external member opens and interacts with the electronic message.

Abstract: Systems and methods for improving search results using assessment based routing are provided. In example embodiments, social network data for members in an organization and members outside the organization is accessed. An internal member and external member may share one or more attributes, such as working on the same project or within the same company. An electronic assessment can be sent to the internal member to generate assessment data about the external member. The assessment data can be included in an electronic message sent to the external member. Interaction data from the electronic message can be received when the external member opens and interacts with the electronic message.

Abstract: Systems and methods for improving search results using social routing are provided. In example embodiments, an affinity metric and match metric are determined for a plurality of users. The affinity metric indicates an interaction level between a given user and the match metric indicates how well the given user matches parameters of a search string. Based on a correlation between the affinity metric and the match metric, one or more items of content may be transmitted to one or more selected users. Improved search results can then be generated based on interactions with the one or more items of content.

Abstract: In an example embodiment, a supervised machine learning algorithm is used to train a communication reply score model based on an extracted first set of features and second set of features from social networking service member profiles and activity and usage information. When a plurality of member search results is to be displayed, for the member identified in each of the plurality of member search results, the member profile corresponding to the member is parsed to extract a third set of one or more features from the member profile, activity and usage information pertaining to actions taken by the members on the social networking service is parsed to extract a fourth set of one or more features, and the extracted third set of features and fourth set of features is inputted into the communication reply score model to generate a communication reply score, which is displayed visually to a searcher.

Abstract: In an example embodiment, a supervised machine learning algorithm is used to train a communication reply score model based on an extracted first set of features and second set of features from social networking service member profiles and activity and usage information. When a plurality of member search results is to be displayed, for the member identified in each of the plurality of member search results, the member profile corresponding to the member is parsed to extract a third set of one or more features from the member profile, activity and usage information pertaining to actions taken by the members on the social networking service is parsed to extract a fourth set of one or more features, and the extracted third set of features and fourth set of features is inputted into the communication reply score model to generate a communication reply score, which is displayed visually to a searcher.