Enhancing-Recommendation-Systems-Comparative-Analysis-of-GNNs-vs-Traditional-Methods.pptx

The Presentation aims to give a undertstanding of how the Graph neural networks are used to optimize the recommendation systems using GNN over traditional methods. It aims to discuss the findings and literature survey of the current state of the art models used for recommendor systems. It starts from shallow models to neural and finally Graph neural networks like GAT, GCN and GraphSAGE etc.

1. Enhancing Recommendation Systems: Analysis of GNNs and Traditional Methods Name: Vaibhav Vijay Rahalkar Division : TE-9 Roll no : 33167 Guide Info: Mr. Sachin D. Shelke, IT dept, PICT Reviewer Info: Mr. Manish R. Khodaskar , IT dept, PICT

2. Objectives To study traditional recommendation system techniques. To study Graph Neural Networks (GNNs) and their application in recommender systems. To explore and compare GNN-based recommendation systems with traditional methods. To explore specific GNN architectures relevant to recommender systems. To study the advantages and limitations of both approaches.

3. Abstract In today’s digital age, recommender systems are essential for managing the vast amount of online information and guiding users towards relevant content. In this case study, I aim to explore the potential of Graph Neural Networks (GNNs) in enhancing recommender systems. GNNs have shown promise in capturing and leveraging the rich structural information present in user-item interaction graphs, making them a compelling area of study for improving recommendation accuracy. This research will focus on investigating various GNN techniques, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), and their ability to model complex relationships in data. The motivation behind this study is to understand how GNNs can be applied to different recommendation tasks and how they compare with traditional methods like collaborative filtering and content-based filtering. By conducting this research, I aim to identify the strengths and weaknesses of GNNs in handling diverse datasets and scenarios, such as e-commerce, streaming services, and social media platforms. The insights gained from this study will provide a deeper understanding of the current state of GNN-based recommender systems and pave the way for future innovations in this field.

4. Traditional Recommendation System Techniques Collaborative Filtering User-item interactions are used to generate recommendations. It faces challenges with data sparsity and cold-start problems. Content-Based Filtering Content-based filtering recommends items based on user profiles and item attributes. It can address cold-start issues but may struggle to provide diverse recommendations. Matrix Factorization Matrix factorization techniques decompose user-item interaction matrices to uncover latent factors. They are powerful but can be computationally intensive and less effective with sparse data.

5. Challenges of Traditional Methods 1 Data Sparsity There may not be enough data to identify meaningful patterns. 2 Cold-Start Problem Dealing with new users or items that lack sufficient interaction data.. 3 Scalability Issues As the size of the data grows, leading to longer processing times and increased resource requirements.

6. Introduction to Graph Neural Networks (GNNs) Overview Graph Neural Networks (GNNs) are a class of neural networks designed to work with graph-structured data, capturing complex relationships between entities (nodes) and their interactions (edges). Advantages GNNs offer several advantages for recommendation systems, including modeling complex dependencies, handling data sparsity, and addressing cold-start problems.

7. GNNs for Recommendation Systems Graph Representation GNNs utilize graph structures in recommendation systems to learn user-item relationships. Specific Architectures GNNs like GCNs, GraphSAGE , and GATs enhance recommendations through graph processing. Advantages of GNN-Based Approaches GNNs improve accuracy and cold-start handling, advancing recommendation systems effectively. .

8. Comparative Analysis and Applications Traditional recommendation methods are simple but struggle with sparsity, cold-start, and scalability. GNNs better address sparsity and cold-start issues but increase computational complexity. GNNs personalize recommendations by utilizing social network interactions and graph structures. Knowledge graphs enhance recommendations by adding context and relationships between entities.

9. Strengths and Weaknesses of GNNs Strengths Weaknesses Enhanced accuracy in modeling complex relationships Increased computational complexity and resource requirements Improved handling of data sparsity and cold-start problems Potential challenges with scalability as data size grows Ability to leverage graph structure and contextual information Requirement for specialized expertise in graph-based techniques

10. Literature Survey Link Link

11. Literature Survey Link Link

12. Real-World Applications of GNNs E-Commerce Recommendations GNNs have been successfully applied in e-commerce platforms to provide personalized product recommendations, leveraging the complex relationships between users, items, and their interactions.

13. Streaming Service Recommendations In the streaming media industry, GNNs have demonstrated their ability to enhance recommendation accuracy and address cold-start problems, leading to improved user engagement and satisfaction. Social Media Recommendations GNNs have been utilized in social media platforms to provide personalized content and connection recommendations, leveraging the graph-like structure of social networks.

14. Future Directions Ongoing Research Ongoing research in GNNs for recommendation systems includes exploring new architectures, integrating GNNs with other advanced techniques, and applying them to emerging domains. Improving Efficiency Future developments may focus on improving the computational efficiency and scalability of GNNs to make them more practical for large-scale recommendation systems.

15. Diverse Data Handling Enhancing GNNs' ability to handle diverse types of data, such as multimedia content and user-generated information, can further expand their applicability in recommendation systems.

16. Conclusion In conclusion, this work lays the foundation for an in-depth study of recommendation systems, beginning with a comparison between traditional techniques and emerging Graph Neural Network (GNN)-based approaches. Traditional methods, while effective, often struggle with challenges like data sparsity and cold-start problems, which GNNs have the potential to address more effectively. Moving forward, the focus will be on exploring specific GNN architectures and their applications in enhancing recommendation systems. This preliminary exploration aims to provide a clearer understanding of the strengths and limitations of each approach, setting the stage for more detailed analysis and application in future work.

17. References [1] Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 . Graph Neural Networks for Social Recommendation . In Proceedings of the 2019 World Wide Web Conference (WWW ’19), May 13–17, 2019, San Francisco, CA, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3308558.3313488 [2] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai , William L. Hamilton, and Jure Leskovec . 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems . In KDD ’18: The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 19–23, 2018, London, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3219819.3219890 [3] Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022 . Graph Neural Networks in Recommender Systems : A Survey. ACM Comput . Surv . 37, 4, Article 111 (April 2022), 37 pages. https://doi.org/10.1145/1122445.1122456

18. References [4] van den Berg, R., Kipf , T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. * arXiv preprint arXiv:1706.02263*. https://doi.org/10.48550/arXiv.1706.02263 [5] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19), July 21–25, 2019, Paris, France. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3331184.3331267 [6] He, X., Liao, L., Zhang, H., Nie , L., Hu, X., & Chua, T.-S. (2017). Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW '17) (pp. 173–182). International World Wide Web Conference Committee (IW3C2). https://doi.org/10.1145/3038912.3052569