Graph-based recommendations service systems and methods

A computing apparatus for providing recommendations, the apparatus comprising a processor and a memory storing instructions that, when executed by the processor, configure the apparatus to:

• obtain a compact graph representation representing a recommendations directed-graph comprising a multiplicity of nodes and a multiplicity of weighted edges, each node of said multiplicity of nodes being associated with type metadata indicating that each node represents one of a multiplicity of recommendable items or one of a multiplicity of non-recommendable items, each weighted edge of said multiplicity of weighted edges joining a source node of said multiplicity of nodes to a target node of said multiplicity of nodes, each weighted edge being associated with edge-weight metadata;
• store said compact graph representation in a primary memory of the computing apparatus;
• receive a multiplicity of recommendation requests, each request requesting a recommendation of at least one recommendable item for a remote user based at least in part on request-context metadata associated with each request;
• process each request of said multiplicity of recommendation requests, including performing at least the following steps for each request:
• selecting an entry node of said multiplicity of nodes based at least in part on said request-context metadata;
• traversing, via said compact graph representation in said primary memory, only a highly-weighted portion of said recommendations-graph that is proximate to said entry node to select a multiplicity of paths leading respectively to a multiplicity of potential recommendation nodes, each of which represents a recommendable item of said multiplicity of recommendable items, said multiplicity of paths being selected based at least in part on weighted-edge-weights of weighted edges making up a given path;
• computing a multiplicity of cumulative node scores corresponding respectively to said multiplicity of potential recommendation nodes, including computing each cumulative node score based at least in part on said weighted-edge-weights of each path of said multiplicity of paths that lead to a given node;
• selecting a recommendation node based at least in part on said multiplicity of cumulative node scores;
• and
• providing in response to each request an identifier identifying a recommendable item represented by said selected recommendation node.

A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, configure the processor to:

• obtain a compact graph representation representing a recommendations directed-graph comprising a multiplicity of nodes and a multiplicity of weighted edges, each node of said multiplicity of nodes being associated with type metadata indicating that each node represents one of a multiplicity of recommendable items or one of a multiplicity of non-recommendable items, each weighted edge of said multiplicity of weighted edges joining a source node of said multiplicity of nodes to a target node of said multiplicity of nodes, each weighted edge being associated with edge-weight metadata;
• store said compact graph representation in a primary memory of the processor;
• receive a multiplicity of recommendation requests, each request requesting a recommendation of at least one recommendable item for a remote user based at least in part on request-context metadata associated with each request;
• process each request of said multiplicity of recommendation requests, including performing at least the following steps for each request:
• selecting an entry node of said multiplicity of nodes based at least in part on said request-context metadata;
• traversing, via said compact graph representation in said primary memory, only a highly-weighted portion of said recommendations directed-graph that is proximate to said entry node to select a multiplicity of paths leading respectively to a multiplicity of potential recommendation nodes, each of which represents a recommendable item of said multiplicity of recommendable items, said multiplicity of paths being selected based at least in part on weighted-edge-weights of weighted edges making up a given path;
• computing a multiplicity of cumulative node scores corresponding respectively to said multiplicity of potential recommendation nodes, including computing each cumulative node score based at least in part on said weighted-edge-weights of each path of said multiplicity of paths that lead to a given node;
• selecting a recommendation node based at least in part on said multiplicity of cumulative node scores;
• and
• providing in response to each request an identifier identifying a recommendable item represented by said selected recommendation node.