28 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
Integration of Deep Sparse Autoencoder and Particle Swarm Optimization to Develop a Recommender System
نوع پژوهش مقاله ارائه شده
کلیدواژه‌ها
recommender systems, deep sparse autoencoder, PSO, trust, tag
پژوهشگران میلاد احمدیان (نفر اول)، محمود احمدی (نفر دوم)، سجاد احمدیان (نفر سوم)، سید محمد جعفر جلالی (نفر چهارم)، عباس خسروی (نفر پنجم)، سعید نهاوندی (نفر ششم به بعد)

چکیده

Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online shopping. Deep neural networks have shown significant improvement in the performance of recommender systems by learning the latent features of users/items based on input data. However, it is a challenging issue to how to apply deep neural networks on different resources and how to integrate their results. In this regard, we propose a recommender system in this paper based on deep sparse autoencoder and particle swarm optimization. In particular, a deep sparse autoencoder is utilized to learn latent features based on the ratings matrix, trust relationships, and tag information. Then, particle swarm optimization is used to find the optimal weights of these latent features in calculating unknown ratings. Experiments on two datasets show the superiority of the proposed method in comparison with state of the art recommender algorithms.