30 فروردین 1403

سجاد احمدیان

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه
تحصیلات: دکترای تخصصی / مهندسی کامپیوتر
تلفن: 09188339565
دانشکده: دانشکده فناوری اطلاعات

مشخصات پژوهش

عنوان
Deep Representation Learning using Multilayer Perceptron and Stacked Autoencoder for Recommendation Systems
نوع پژوهش مقاله ارائه شده
کلیدواژه‌ها
Recommendation systems, Representation learning, Deep learning, Collaborative filtering
پژوهشگران امیر خانی ینگی کند (نفر اول)، مجید مقدادی (نفر دوم)، سجاد احمدیان (نفر سوم)، سید محمد جعفر جلالی (نفر چهارم)، عباس خسروی (نفر پنجم)، سعید نهاوندی (نفر ششم به بعد)

چکیده

Deep learning-based collaborative filtering methods are studied in recommendation systems as efficient feature mapping techniques. The aim of these methods is to project the users and items to a common representation space and obtain their latent features. Although these methods have been widely used in the literature, they suffer from the limited expressiveness of Dot product function. In other words, Dot product cannot describe different impacts of various latent factors. To solve this issue, we propose a novel recommender system named Deep-MSR which exploits the multilayer perceptron (MLP) neural network and stacked auto-encoder network (SAN) to extract item latent factors and user latent factors from user-item interaction matrix. The obtained latent factors are used in the proposed rating prediction module which integrates user preferences and item features in the recommendation process. Our experiments on two well-known datasets show that our method can outperform the competitive baseline recommendation methods.