Friendship-based recommender system

A system, comprising:

• a memory including computer executable instructions;
• and
• a processor configured to execute the computer executable instructions and cause the system to:
• receive electronic record data indicating interactions between a plurality of users and a plurality of providers;
• construct a bipartite graph based on the interactions, wherein one set of nodes of the bipartite graph represents the plurality of users and another set of nodes of the bipartite graph represents the plurality of providers, and wherein an edge connects each respective provider of the plurality of providers with each respective user of the plurality of users that interacted with the respective provider in the interactions;
• identify, for each user of the plurality of users, a set of other users in the plurality of users, wherein each given user in the set of other users interacted with at least one common provider that the user interacted with in the interactions;
• add to the bipartite graph, for each user of the plurality of users, intra-user edges between the user and the set of other users in the plurality of users;
• compute, for each respective intra-user edge of the intra-user edges, a weight of the respective intra-user edge based on a number of common providers shared by a pair of users of the plurality of users that is connected by the respective intra-user edge;
• compute, for each respective user of the plurality of users, a popularity score, based on the weights of the intra-user edges connected to the respective user, wherein to compute, for each respective user of the plurality of users, the popularity score, the processor is configured to:
• determine that a weight of a particular intra-user edge connected to the respective user does not meet a threshold weight;
• set the weight of the particular intra-user edge to zero;
• and
• compute an average weight based on the weights of the intra-user edges connected to the respective user;
• compute, for each respective provider of the plurality of providers, a reputation score based on the popularity scores of respective users of the plurality of users that are connected to the respective provider;
• and
• train a recommender system using the reputation scores of the plurality of providers, wherein the recommender system is used to automatically determine a provider recommendation.

A non-transitory computer readable medium comprising instructions to be executed in a computer system, wherein the instructions when executed in the computer system perform a method on a computing device, comprising:

• receiving electronic record data indicating interactions between a plurality of users and a plurality of providers;
• constructing a bipartite graph based on the interactions, wherein one set of nodes of the bipartite graph represents the plurality of users and another set of nodes of the bipartite graph represents the plurality of providers, and wherein an edge connects each respective provider of the plurality of providers with each respective user of the plurality of users that interacted with the respective provider in the interactions;
• identifying, for each user of the plurality of users, a set of other users in the plurality of users, wherein each given user in the set of other users interacted with at least one common provider that the user interacted with in the interactions;
• adding to the bipartite graph, for each user of the plurality of users, intra-user edges between the user and the set of other users in the plurality of users;
• computing, for each respective intra-user edge of the intra-user edges, a weight of the respective intra-user edge based on a number of common providers shared by a pair of users of the plurality of users that is connected by the respective intra-user edge;
• computing, for each respective user of the plurality of users, a popularity score, based on the weights of the intra-user edges connected to the respective user, wherein computing, for each respective user of the plurality of users, the popularity score comprises:
• determining that a weight of a particular intra-user edge connected to the respective user does not meet a threshold weight;
• setting the weight of the particular intra-user edge to zero;
• and
• computing an average weight based on the weights of the intra-user edges connected to the respective user;
• computing, for each respective provider of the plurality of providers, a reputation score based on the popularity scores of respective users of the plurality of users that are connected to the respective provider;
• and
• training a recommender system using the reputation scores of the plurality of providers, wherein the recommender system is used to automatically determine a provider recommendation.