چکیده مقاله
The integration of deep learning DL into recommender systems RS has significantly reshaped how personalized content is generated and delivered across diverse domains Traditional recommendations such as collaborative filtering and content based filtering struggle to cope with the increasing complexity, diversity, and sparsity inherent in modern user item data DL techniques, however, can learn rich, non linear mappings from multi modal and large scale data inputs This is a comprehensive survey that synthesizes the outcome of 40 peer reviewed papers published in the time period 2023 2025 to provide a fine level taxonomy of DL architectures like CNNs, RNNs, Transformers, GNNs, and Autoencoders with multimodal and hybrid architectures We categorize and compare and contrast these models in terms of methodology, application area e g , healthcare, academia, streaming media, e commerce , and key challenge areas like cold start, scalability, interpretability, and fairness Furthermore, this paper advocates for an integrated pipeline through AutoML, federated learning, and pretraining with contrast to overcome the barriers related to personalization, privacy, and versatility Through state of the art model benchmarking and future trends such as LLM based personalization and ethics aware design, this survey not only recapitulates latest progress but also charts the future direction to the next generation of trustworthy and intelligent recommender systems
کلیدواژهها
نویسندگان
Saba Kheirkhah Kheirabadi
Department of Computer, CT C , Islamic Azad University, Tehran, Iran
Azita Shirazipour
Department of Computer, CT C , Islamic Azad University, Tehran, Iran
Seyed Javad Mirabedini
Department of Computer, CT C , Islamic Azad University, Tehran, Iran
شیوه ارجاع
Kheirkhah Kheirabadi, Saba and Shirazipour, Azita and Mirabedini, Seyed Javad,1404,A Comprehensive Review of Deep Learning Integration in Recommender Systems: Taxonomy, Challenges, and Future Directions,The Second National Conference on the Era of Technology Explosion: Artificial Intelligence, a Transformation in Industry, Trade, and Supply Chain,Tabriz
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی عصر انفجار تکنولوژی؛ هوش مصنوعی، تحولی در صنعت، تجارت و زنجیره تامین17 مهر 1404 · تبریز