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Cross Domain Recommendation By Using Deep Learning

  • Unique Paper ID: 165180
  • Volume: 11
  • Issue: 1
  • PageNo: 229-232
  • Abstract:
  • In this project, we propose a novel approach for cross-domain recommendation, specifically targeting books and movies, utilizing deep learning techniques. Traditional recommendation systems often struggle when tasked with suggesting items across different domains due to the inherent differences in their features and user preferences. Deep learning, with its ability to learn intricate patterns and representations from raw data, offers a promising solution to this challenge. Our methodology involves the development of a deep learning model capable of capturing the complex relationships between users, items, and their attributes in both the book and movie domains. We leverage techniques such as collaborative filtering and neural network architectures to extract meaningful representations from user interaction data. Additionally, we incorporate content-based features to enhance recommendation accuracy and diversity. To evaluate the effectiveness of our approach, we conduct experiments on real-world datasets encompassing both book and movie ratings. We compare the performance of our deep learning-based recommendation model against baseline methods, collaborative filtering and matrix factorization approaches. Our results demonstrate the superior recommendation quality and cross-domain adaptability of the proposed model, showcasing its potential for personalized and diverse recommendation systems in heterogeneous domains.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 1
  • PageNo: 229-232

Cross Domain Recommendation By Using Deep Learning

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