Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Nhúng sự khác biệt cho hệ thống gợi ý
Tóm tắt
Bài báo này đề xuất một chiến lược huấn luyện điểm mới lạ và đơn giản, mang tên nhúng sự khác biệt (DifE), cho các hệ thống gợi ý nhằm nắm bắt thông tin cá nhân hóa được định hình từ sự khác biệt trong sở thích theo cặp, đồng thời sử dụng huấn luyện điểm hiệu quả và hiệu suất cao. Cụ thể, một hàm đã được thiết kế để nắm bắt và làm nổi bật sự khác biệt trong sở thích theo cặp. Sau đó, một phép chiếu mới đã được sử dụng để xây dựng một không gian mới trong đó thông tin theo cặp được bảo tồn, và hàm mất mát theo điểm mới được đề xuất là đủ để học được một nhúng tốt hơn. Để xác minh sự ưu việt và tổng quát của chiến lược được đề xuất, chúng tôi tích hợp hàm mất mát đề xuất với bốn hệ thống gợi ý hiện đại và thu được bốn mô hình tối ưu tương ứng là MF-DifE, NeuMF-DifE, GCN-DifE và SGL-DifE. Kết quả thí nghiệm toàn diện và so sánh trên ba tập dữ liệu công khai cho thấy rằng các mô hình tối ưu này đạt được sự cải thiện đáng kể so với các mô hình cơ sở tương ứng và vượt trội hơn nhiều phương pháp gợi ý gần đây khác, điều này cho thấy sự xuất sắc và tính tổng quát của hàm mất mát đề xuất.
Từ khóa
Tài liệu tham khảo
Bell R, Koren Y, Volinsky C (2007) Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 95–104
Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263
Cai D, Hu J, Qian S, Fang Q, Zhao Q, Xu C (2021) Grecx: an efficient and unified benchmark for GNN-based recommendation, arXiv preprint arXiv:2111.10342
Castells P, Hurley N, Vargas S (2022) Novelty and diversity in recommender systems. In: Recommender systems handbook, Springer, pp 603–646
Deng Z, Huang L, Wang C, Lai J, Yu P (2019) DeepCF: a unified framework of representation learning and matching function learning in recommender system. In: Proceedings of the 33rd AAAI conference on artificial intelligence, pp 61–68
Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the 31st AAAI conference on artificial intelligence, pp 1309–1315
Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: Proceedings of the 41st ACM SIGIR international conference on research and development in information retrieval, pp 515–524
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th AISTATS international conference on artificial intelligence and statistics, JMLR workshop, pp 249–256
Han X, Shi C, Wang S, Philip S.Y, Song L (2018) Aspect-level deep collaborative filtering via heterogeneous information networks. In: Proceedings of the 27th IJCAI international joint conference on artificial intelligence, pp 3393–3399
He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd ACM SIGIR international conference on research and development in information retrieval, pp 639–648
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. International world wide web conferences steering committee, pp 173–182
He X, Zhang H, Kan M.Y, Chua T.S (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th ACM SIGIR international conference on research and development in information retrieval, pp 549–558
Jain A, Nicholls A (2008) Recommendations for evaluation of computational methods. In: Journal of Computer-Aided Molecular Design, 22(3): pp 133–139
Jiang J, Yang D, Xiao Y, Shen C (2019) Convolutional gaussian embeddings for personalized recommendation with uncertainty, In: Proceedings of the 28th IJCAI international joint conference on artificial intelligence, pp 2642–2648
Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pp 233–240
Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 426–434
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. In: Computer, 42(8): pp 30–37
Li X, She J (2017) Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 305–314
Liu T, Liao J, Wu Z, Wang Y, Wang J (2019) A geographical-temporal awareness hierarchical attention network for next point-of-interest recommendation. In: Proceedings of the ACM ICMR 2019 international conference on multimedia retrieval, pp 7–15
Liu J, Wang Y, Wang G, Yin F (2016) Personalized recommendation of live programs in cable television. In: Proceeding of the 5th ICCSNT international conference on computer science and network technology, pp 268–272
Ma J, Cui P, Kuang K, Wang X, Zhu W (2019) Disentangled graph convolutional networks. In: Proceedings of the 36th ICML international conference on machine learning, pp 4212–4221
Mao K, Zhu J, Xiao X, Lu B, Wang Z, He X (2021) Ultragcn: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM CIKM international conference on information and knowledge management, pp 1253–1262
Matuszyk P, Spiliopoulou M (2015) Semi-supervised learning for stream recommender systems. In: Proceedings of the 18th DS international conference on discovery science, pp 131–145
Mnih A, Salakhutdinov R.R (2008) Probabilistic matrix factorization. In: Proceedings of the NIPS 2007 advances in neural information processing systems 20, pp 1257–1264
Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the EMNLP 2014 conference on empirical methods in natural language processing, pp 1532–1543
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th UAI conference on uncertainty in artificial intelligence, pp 452–461
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook, pp 1–35
Symeonidis P, Coba L, Zanker M (2019) Counteracting the filter bubble in recommender systems: novelty-aware matrix factorization. In: Intelligenza ArtificialeIntell 13(1): pp 37–47
Wang D, Zhang X, Yu D, Xu G, Deng S (2020) Came: content-and context-aware music embedding for recommendation. In: IEEE Transactions on Neural Networks and Learning Systems 32(3): pp 1375–1388
Symeonidis P, Coba L, Zanker M (2019) Counteracting the filter bubble in recommender systems: novelty-aware matrix factorization. In: Intelligenza ArtificialeIntell 13(1): pp 37–47
Wang D, Zhang X, Yu D, Xu G, Deng S (2020) Came: content-and context-aware music embedding for recommendation. In: IEEE Transactions on Neural Networks and Learning Systems 32(3): pp 1375–1388
Wang D, Wang X, Xiang Z, Yu D, Deng S, Xu G (2021) Attentive sequential model based on graph neural network for next poi recommendation. In: Proceedings of the 30th WWW international conference on world wide web, pp 2161–2184
Wang X, He X, Wang M, Feng F, Chua T.S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd ACM SIGIR international conference on research and development in information retrieval, pp 165–174
Wang H, Wang N, Yeung D.Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244
Wu L (2020) Advances in collaborative filtering and ranking. PhD thesis, University of California, Davis
Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021) Selfsupervised graph learning for recommendation. In: Proceedings of the 44th ACM SIGIR international conference on research and development in information retrieval, pp 726–735
Xue H, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: In: Proceedings of the 26th IJCAI international joint conference on artificial intelligence, pp 3203–3209
Xu Y, Zhang Y, Guo W, Guo H, Tang R, Coates M (2020) Graphsail: graph structure aware incremental learning for recommender systems. In: Proceedings of the 29th ACM CIKM international conference on information and knowledge management, pp 2861–2868
Yang J, Chen C, Wang C, Tsai M.F (2018) Hop-rec: high-order proximity for implicit recommendation. In: Proceedings of the 12th ACM conference on recommender systems, pp 140–144
Ying R, He R, Chen K, Eksombatchai P, Hamilton W.L, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 974–983
Zhang J, Shi X, Zhao S, King I (2019) Star-GCN: stacked and reconstructed graph convolutional networks for recommender systems. arXiv preprint arXiv:1905.13129
Zhang S, Yao L, Xu X, Wang S, Zhu L (2017) Hybrid collaborative recommendation via semi-autoencoder. In: Proceedings of the 23rd ICONIP international conference on neural information processing, pp 185–193
Zheng L, Lu C, Jiang F, Zhang J, Yu P.S (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM conference on recommender systems, pp 311–319