{'fraction_non_alphanumeric': 0.024504084014002333, 'fraction_numerical': 0.0, 'mean_word_length': 5.085106382978723, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.