Advancements and Innovations in Recommendation Systems: From Traditional Algorithms to Deep Learning Evolution

DOI

10.2991/978-94-6463-512-6_67How to use a DOI?

Keywords

Recommender Systems; Collaborative Filtering; Multimodal Fusion; Privacy Preservation

Abstract

In the digital age, recommender systems have become instrumental in managing information overload by delivering personalized content recommendations. This paper conducts a thorough review of the evolution of deep learning techniques in recommender systems, tracing their development from the initial collaborative filtering methods to the sophisticated use of graph neural networks and knowledge graphs. The study demonstrates that deep learning significantly enhances recommender system capabilities in delivering personalized recommendations, efficiently processing multimodal data, and bolstering user privacy protection. The analysis highlights that early recommender systems primarily relied on collaborative filtering, which, despite its effectiveness, faced challenges such as data sparsity and scalability. The integration of deep learning has revolutionized these systems, enabling the extraction of complex features and patterns from vast datasets. Techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers have proven effective in capturing nuanced user preferences and item features. Furthermore, the advent of graph neural networks and knowledge graphs has introduced advanced capabilities for handling relational data and incorporating semantic information, significantly improving recommendation accuracy and contextual relevance.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ningxin Tan
PY  - 2024
DA  - 2024/09/23
TI  - Advancements and Innovations in Recommendation Systems: From Traditional Algorithms to Deep Learning Evolution
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 638
EP  - 644
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_67
DO  - 10.2991/978-94-6463-512-6_67
ID  - Tan2024
ER  -