Application of hybrid recommendation system model based on graph neural networks and feature description of users and communities

In the conditions of growing competition in the social media market, there is an increasing need for personalized content recommendation tools that can increase user engagement. Traditional approaches to building recommendation systems based on collaborative filtering or content filtering have limitations in community recommendation tasks, where both the structure of social connections and the semantic description of groups are important. This article is aimed at studying a hybrid model of a recommendation system combining graph neural networks and feature description of users and communities. The model integrates information about user interactions with groups in the form of a graph, text embeddings of community descriptions obtained using transformer models, as well as numerical and categorical features of groups and users. The results of the study demonstrate a significant advantage of the proposed approach compared to baseline methods. A comparative analysis with popularity baseline and random baseline shows an improvement in the MAP@3 metric by 3.89 times relative to recommending popular groups and by 4045.9 times relative to random recommendation. The study is based on data from the OK social network, including information about 159,685 users and 54,425 communities.

1. Koren Y., Bell R., Volinsky Ch. Matrix Factorization Techniques for Recommender Systems. Computer. 2009;42(8):30–37. https://doi.org/10.1109/MC.2009.263

2. Guo H., Tang R., Ye Y., et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In: IJCAI '17: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 19–25 August 2017, Melbourne, Australia. International Joint Conferences on Artificial Intelligence; 2017. P. 1725–1731. https://doi.org/10.24963/ijcai.2017/239

3. Wu Z., Pan Sh., Chen F., et al. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 2021;32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

4. Zhou G., Zhu X., Song Ch., et al. Deep Interest Network for Click-Through Rate Prediction. In: KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 19–23 August 2018, London, UK. New York: ACM; 2018. P. 1059–1068. https://doi.org/10.1145/3219819.3219823

5. Wang X., He X., Wang M., et al. Neural Graph Collaborative Filtering. In: SIGIR '19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 21–25 July 2019, Paris, France. New York: ACM; 2019. P. 165–174. https://doi.org/10.1145/3331184.3331267

6. He X., Deng K., Wang X., et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In: SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 25–30 July 2020, Virtual Event. New York: ACM; 2020. P. 639–648. https://doi.org/10.1145/3397271.3401063

7. Fan W., Ma Y., Li Q., et al. Graph Neural Networks for Social Recommendation. In: WWW '19: The World Wide Web Conference, 13–17 May 2019, San Francisco, CA, USA. New York: ACM; 2019. P. 417–426. https://doi.org/10.1145/3308558.3313488

8. Devlin J., Chang M.-W., Lee K., et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: NAACL-HLT 2019: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 02–07 June 2019, Minneapolis, MN, USA. Association for Computational Linguistics; 2019. P. 4171–4186. https://doi.org/10.18653/v1/N19-1423

9. Rendle S., Freudenthaler Ch., Gantner Z., et al. BPR: Bayesian Personalized Ranking from Implicit Feedback. In: UAI '09: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 18–21 June 2009, Montreal, QC, Canada. Corvallis: AUAI Press; 2009. P. 452–461.

10. Ying R., He R., Chen K., et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In: KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 19–23 August 2018, London, UK. New York: ACM; 2018. P. 974–983. https://doi.org/10.1145/3219819.3219890