Abstract: Systems and methods for generating real-time, personalized recommendations are disclosed. In one embodiment, a method operates upon an electronic data collection organized as a network of vertices and edge connections between the vertices. The method provides the recommendations includes iteratively traversing across edges that satisfy search criteria to a new set of vertices and filtering each new set of vertices to satisfy the search criteria. At the conclusion of the traversing and filtering, a final set of vertices represents the recommended entities. In some embodiments, a control vector describes a sequence of relationships between a requester and the items to be recommended. The method can assign scores to candidate recommendations and select the recommendations having the highest scores. Advantageously, the method provides flexibility and rapid execution of recommendation queries without the need to precompute intermediate results.

Abstract: Systems and methods for generating real-time, personalized recommendations are disclosed. In one embodiment, a method operates upon an electronic data collection organized as a network of vertices and edge connections between the vertices. The method provides the recommendations includes iteratively traversing across edges that satisfy search criteria to a new set of vertices and filtering each new set of vertices to satisfy the search criteria. At the conclusion of the traversing and filtering, a final set of vertices represents the recommended entities. In some embodiments, a control vector describes a sequence of relationships between a requester and the items to be recommended. The method can assign scores to candidate recommendations and select the recommendations having the highest scores. Advantageously, the method provides flexibility and rapid execution of recommendation queries without the need to precompute intermediate results.