DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation
المؤلفون المشاركون
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-30
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade.
Learning effective latent factors plays the most important role in recommendation methods.
Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem.
To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content.
Deep neural networks have emerged as very appealing in learning effective representations in many applications.
In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata.
In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items.
Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines.
We provide further insight into the design selections and hyperparameters of our recommendation method.
In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Gan, Mingxin& Zhang, Hang. 2020. DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1142083
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Gan, Mingxin& Zhang, Hang. DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1142083
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Gan, Mingxin& Zhang, Hang. DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1142083
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142083
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر