An implementation of a Distributed Stochastic Gradient Descent for Recommender Systems based on Map-Reduce

This work presents an implementation of a Distributed Stochastic Gradient Descent (DSGD) for Recommender Systems based on Hadoop/MapReduce. Recommender Systems aim at presenting first the information in which users may be more interested. To do this, they analyse a great volume of data that represent the users preferences (e.g. ratings). Thus, this stirs up the need of load-balancing. DSGD is a Matrix Factorization technique that has demonstrated high accuracy and scalability. In this work we expose this algorithm and modify it to improve its accuracy and adaptability to a hadoop cluster. The experimentation phase uses Movie-Lens datasets. Comparisons with other algorithms are given. Results show the good performance of the implementation.