Fmr Corp.

Shishir Shekhar

Alex Arias-Vargas

Serdar Kadioglu

Bibhash Chakrabarty

Bernard Kleynhans

Akshay Jain

Anshuman Pradhan

Shovon Sengupta

Pramod R

Debalina Gupta

Methods and apparatuses are described for digital content classification and recommendation based upon reinforcement learning. A server converts unstructured text corresponding to each digital content item into a content item feature set. The server generates a user context vector associated with a plurality of users. The server trains a linear multi-armed bandit (MAB) classification model based upon the user context vectors and historical user content recommendation information. The server receives a new user context vector associated with a new user. The server executes the MAB model using the new user context vector as input to generate content interaction prediction scores. The server selects the content interaction prediction scores above a predetermined threshold and identifies the associated digital content item. The server presents the identified digital content items on a client device and receives a response. The server updates linear UCB coefficient vectors of the MAB model based upon the response.

Digital content classification and recommendation based upon artificial intelligence reinforcement learning

Methods and apparatuses are described for automated predictive product recommendations using reinforcement learning. A server captures historical activity data associated with a plurality of users. The server generates a context vector for each user, the context vector comprising a multidimensional array corresponding to historical activity data. The server transforms each context vector into a context embedding. The server assigns each context embedding to an embedding cluster. The server determines, for each context embedding, (i) an overall likelihood of successful attempt and (ii) an incremental likelihood of success associated products available for recommendation. The server calculates, for each context embedding, an incremental income value associated with each of the likelihoods of success. The server aggregates (i) the overall likelihood of successful attempt, (ii) the likelihoods of success, and (iii) the incremental income values into a recommendation matrix. The server generates instructions to recommend products based upon the recommendation matrix.

Automated predictive product recommendations using reinforcement learning

Methods and apparatuses are described for automated predictive call routing using reinforcement learning. A server captures a bitstream of an incoming call from a first client device, the bitstream including metadata comprising attributes of the incoming call and attributes of a user of the device. The server determines an identity of the user based upon the metadata. The server generates a first context vector comprising a multidimensional array corresponding to the metadata. The server inserts the first vector into a high-dimensional vector space comprising historical context vectors, each historical vector (i) corresponding to metadata associated with a historical call and (ii) associated with an income value and a routing decision. The server determines historical vectors in proximity to the first vector. The server identifies one of the determined historical vectors with an optimal income value and routes the bitstream to a second device using the routing decision.

Automated predictive call routing using reinforcement learning

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Bengaluru, India

Shishir Shekhar