DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

المؤلفون المشاركون

Gan, Mingxin
Zhang, Hang

المصدر

Complexity

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

الفلسفة

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