Generating multi-dimensional recommendation data objects based on decentralized crowd sourcing

A method for decentralized crowd sourced generation of recommendation data objects, the method comprising:

• training, by one or more processors and using a supervisory training function, a machine learning collaborative filtering model, using (i) a plurality of feature vectors corresponding to a plurality of user identifiers and (ii) known therapy efficacy data, to generate a trained machine learning collaborative filtering model;
• receiving, by the one or more processors, a recommendation data object request originating from an external computing device, the recommendation data object request comprising a user identifier and one or more user attributes;
• inputting, by the one or more processors, the one or more user attributes into the trained machine learning collaborative filtering model;
• generating, by the one or more processors and using the trained machine learning collaborative filtering model, one or more therapy identifiers associated with one or more therapy efficacy scores exceeding a therapy efficacy score threshold for one or more of the one or more user attributes;
• providing, by the one or more processors and to the external computing device in response to the recommendation data object request, a recommendation data object comprising one or more of the one or more therapy identifiers, causing the external computing device to provide, based at least in part on the recommendation data object, a therapy feedback object for generating a therapy efficacy transaction block for a distributed ledger, wherein the therapy efficacy transaction block comprises a therapy efficacy score for the user identifier and a therapy identifier;
• and
• retraining, by the one or more processors and using a reinforcement learning function, the trained machine learning collaborative filtering model using (i) the plurality of feature vectors and (ii) a plurality of trusted efficacy blocks of the distributed ledger, wherein the plurality of trusted efficacy blocks is generated by:
• generating, based at least in part on a therapy efficacy model, a plurality of therapy efficacy transaction blocks based at least in part on (i) a respective therapy efficacy score and (ii) one or more respective efficacy indication objects associated with each of a plurality of therapy identifiers, and
• generating, based at least in part on an efficacy consensus protocol, a trusted efficacy block from the distributed ledger in response to a number of the plurality of therapy efficacy transaction blocks satisfying a therapy efficacy transaction block threshold.

A computer-readable medium comprising at least one non-transitory storage medium storing instructions that, when executed by one or more processors, cause an apparatus to:

• train, using a supervisory training function, a machine learning collaborative filtering model, using (i) a plurality of feature vectors corresponding to a plurality of user identifiers and (ii) known therapy efficacy data, to generate a trained machine learning collaborative filtering model;
• receive, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes;
• input the one or more user attributes into the trained machine learning collaborative filtering model;
• generate, using the trained machine learning collaborative filtering model, one or more therapy identifiers associated with one or more therapy efficacy scores exceeding a therapy efficacy score threshold for one or more of the one or more user attributes;
• provide, to the external computing device and in response to the recommendation data object request, a recommendation data object comprising one or more of the one or more therapy identifiers, causing the external computing device to provide, based at least in part on the recommendation data object, a therapy feedback object for generating a therapy efficacy transaction block for a distributed ledger, wherein the therapy efficacy transaction block comprises a therapy efficacy score for the user identifier and a therapy identifier;
• and
• retrain, using a reinforcement learning function, the trained machine learning collaborative filtering model using (i) the plurality of feature vectors and (ii) a plurality of trusted efficacy blocks of the distributed ledger, wherein the plurality of trusted efficacy blocks is generated by:
• generating, based at least in part on a therapy efficacy model, a plurality of therapy efficacy transaction blocks based at least in part on (i) a respective therapy efficacy score and (ii) one or more respective efficacy indication objects associated with each of a plurality of therapy identifiers, and
• generating, based at least in part on an efficacy consensus protocol, a trusted efficacy block from the distributed ledger in response to a number of the plurality of therapy efficacy transaction blocks satisfying a therapy efficacy transaction block threshold.