Recommender systems are ubiquitous in today's marketplace and have great commercial importance, as evidenced by the large number of companies that sell recommender systems solutions. Successful recommender systems use past product purchase and satisfaction data to make high quality personalized recommendations. The vast amounts of data available to recommender systems today forces a total re-evaluation of the methods used to compute recommendations. In this paper, we provide an overview of recommender systems in the era of Big Data. We highlight prevailing recommendation algorithms and how they have been adapted to operate in parallel and distributed computing environments. Within the recommender systems context, we focus our discussion on two specific challenges: how to scale up finding nearest neighbors and how to scale latent factor recommendation methods.
Anastasiu, David C.; Christakopoulou, Evangelia; Smith, Shaden; Sharma, Mohit; Karypis, George.
Big Data and Recommender Systems.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.