An Efficient Deep Learning Model for Recommender Systems

  • Kourosh Modarresi
  • Jamie Diner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


Recommending the best and optimal content to user is the essential part of digital space activities and online user interactions. For example, we like to know what items should be sent to a user, what promotion is the best one for a user, what web design would fit a specific user, what ad a user would be more susceptible to or what creative cloud package is more suitable to a specific user.

In this work, we use deep learning (autoencoders) to create a new model for this purpose. The previous art includes using Autoencoders for numerical features only and we extend the application of autoencoders to non-numerical features.

Our approach in coming up with recommendation is using “matrix completion” approach which is the most efficient and direct way of finding and evaluating content recommendation.


Recommender systems Artificial intelligence Deep learning 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Adobe Inc.San JoseUSA

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