Abstract
The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future.
- [1] . 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, 811–820.Google ScholarDigital Library
- [2] . 2017. Translation-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 161–169.Google ScholarDigital Library
- [3] . 2016. Fusing similarity models with Markov chains for sparse sequential recommendation. In Proceedings of the 16th IEEE International Conference on Data Mining. IEEE Computer Society, 191–200.Google ScholarCross Ref
- [4] . 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 505–514.Google ScholarDigital Library
- [5] . 2018. Self-attentive sequential recommendation. In Proceedings of the 18th IEEE International Conference on Data Mining. IEEE Computer Society, 197–206.Google ScholarCross Ref
- [6] . 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 843–852.Google ScholarDigital Library
- [7] . 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939. Retrieved from https://arxiv.org/abs/1511.06939.Google Scholar
- [8] . 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 565–573.Google ScholarDigital Library
- [9] . 2020. Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. ACM, 322– 330.Google ScholarDigital Library
- [10] . 2020. Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks. Future Gener. Comput. Syst. 108, (2020), 1119–1128.Google ScholarCross Ref
- [11] . 2021. An attention-based spatiotemporal LSTM network for next POI recommendation. IEEE Trans. Serv. Comput. 14, 6 (2021), 1585--1597.Google Scholar
- [12] . 2016. Context-aware sequential recommendation. In Proceedings of the 16th IEEE International Conference on Data Mining. IEEE Computer Society, 1053–1058.Google ScholarCross Ref
- [13] . 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1269–1278.Google ScholarDigital Library
- [14] . 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 165–174.Google ScholarDigital Library
- [15] . 2020. Intention nets: Psychology-inspired user choice behavior modeling for next-basket prediction. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, 6259–6266.Google ScholarCross Ref
- [16] . 2021. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. ijcai.org, 2483–2489.Google ScholarCross Ref
- [17] . 2020. Memory augmented graph neural networks for sequential recommendation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, 5045–5052.Google ScholarCross Ref
- [18] . 2021. Sequential recommendation with graph neural networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 378–387.Google ScholarDigital Library
- [19] . 2021. RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recommendation. In Proceedings of the Web Conference 2021. ACM, 2968–2979.Google ScholarDigital Library
- [20] . 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 974–983.Google ScholarDigital Library
- [21] . 2017. Graph convolutional matrix completion. arXiv:1706.02263. Retrieved from https://arxiv.org/abs/1706.02263.Google Scholar
- [22] . 2018. Spectral collaborative filtering. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 311–319.Google ScholarDigital Library
- [23] . 2019. IntentGC: A scalable graph convolution framework fusing heterogeneous information for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2347–2357.Google ScholarDigital Library
- [24] . 2020. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI Press, 27–34.Google ScholarCross Ref
- [25] . 2020. Neighbor interaction aware graph convolution networks for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1289–1298.Google ScholarDigital Library
- [26] . 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 639–648.Google ScholarDigital Library
- [27] . 2018. Translation-based factorization machines for sequential recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 63–71.Google ScholarDigital Library
- [28] . 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- [29] . 2017. Neural attentive session-based recommendation. In Proceedings of the 26th ACM Conference on Information and Knowledge Management. ACM, 1419–1428.Google ScholarDigital Library
- [30] . 2018. Sequential recommendation with user memory networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 108–116.Google ScholarDigital Library
- [31] . 2019. A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 345–354.Google ScholarDigital Library
- [32] . 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 130–137.Google ScholarDigital Library
- [33] . 2019. Hierarchical gating networks for sequential recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 825–833.Google ScholarDigital Library
- [34] . 2019. Online purchase prediction via multi-scale modeling of behavior dynamics. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2613–2622.Google ScholarDigital Library
- [35] . 2017. Attention is all you need. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems. nips.cc, 5998–6008.Google Scholar
- [36] . 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 1441–1450.Google ScholarDigital Library
- [37] . 2019. Dual sequential prediction models linking sequential recommendation and information dissemination. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 447–457.Google ScholarDigital Library
- [38] . 2018. CSAN: Contextual self-attention network for user sequential recommendation. In Proceedings of the 26th ACM International Conference on Multimedia. ACM, 447–455.Google ScholarDigital Library
- [39] . 2020. Sequential recommendation with dual side neighbor-based collaborative relation modeling. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining. ACM, 465–473.Google ScholarDigital Library
- [40] . 2021. DAN-SNR: A deep attentive network for social-aware next point-of-interest recommendation. ACM Trans. Internet Techn. 21, 1 (2021), 2:1–2:27.Google ScholarDigital Library
- [41] . 2020. Sequential recommendation with self-attentive multi-adversarial network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 89–98.Google ScholarDigital Library
- [42] . 2020. S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM, 1893–1902.Google ScholarDigital Library
- [43] . 2021. Self-supervised hypergraph convolutional networks for session-based recommendation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence. AAAI Press, 4503–4511.Google ScholarCross Ref
- [44] . 2020. Next-item recommendation with sequential hypergraphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1101–1110.Google ScholarDigital Library
- [45] . 2019. Hypergraph neural networks. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. AAAI Press, 3558–3565.Google ScholarDigital Library
- [46] . 2019. HyperGCN: A new method for training graph convolutional networks on hypergraphs. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. nips.cc, 1509–1520.Google Scholar
- [47] . 2020. Temporal heterogeneous interaction graph embedding for next-item recommendation. In Proceedings of the 2020 European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Part III, 314–329.Google Scholar
- [48] . 2017. Inductive representation learning on large graphs. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems. nips.cc, 1024–1034.Google Scholar
- [49] . 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 7–10.Google ScholarDigital Library
- [50] . 2020. Inductive representation learning on temporal graphs. In Proceedings of the 8th International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=rJeW1yHYwH.Google Scholar
- [51] . 2018. ATRank: An attention-based user behavior modeling framework for recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. AAAI Press, 4564–4571.Google ScholarCross Ref
- [52] . 2019. How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=ryGs6iA5Km.Google Scholar
- [53] . 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. ACM, 173–182.Google ScholarDigital Library
- [54] . 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https://arxiv.org/abs/1412.6980.Google Scholar
- [55] . 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web. ACM, 507–517.Google ScholarDigital Library
- [56] . 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 188–197.Google ScholarCross Ref
- [57] . 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 452–461.Google ScholarDigital Library
- [58] . 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939. Retrieved from https://arxiv.org/abs/1511.06939.Google Scholar
- [59] . 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics 4171–4186.Google Scholar
- [60] . 2020. Dynamic graph collaborative filtering. In Proceedings of the 20th IEEE International Conference on Data Mining. IEEE Computer Society, 322–331.Google ScholarCross Ref
- [61] . 2020. Graph neural networks: A review of methods and applications. AI Open 1, (2020), 57–81.Google ScholarCross Ref
- [62] . 2006. Dimensionality reduction by learning an invariant mapping. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Volume 2, 1735–1742.Google ScholarDigital Library
- [63] . 2020. Attribute graph neural networks for strict cold start recommendation. IEEE Trans. Knowl. Data Eng., (2020). Retrieved from https://doi.org/10.1109/TKDE.2020.3038234Google Scholar
- [64] . 2019. Next and next new POI recommendation via latent behavior pattern inference. ACM Trans. Inf. Syst. 37, 4 (2019), 46:1–46:28.Google ScholarDigital Library
- [65] . 2018. A hybrid model based on the rating bias and textual bias for recommender systems. In Proceedings of the 25th International Conference on Neural Information Processing. Springer, Part II, 203–214.Google ScholarDigital Library
- [66] . 2021. Combating selection biases in recommender systems with a few unbiased ratings. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. ACM, 427–435.Google ScholarDigital Library
- [67] . 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 1748–1757.Google ScholarDigital Library
- [68] . 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597–1607.Google ScholarDigital Library
- [69] . 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 9726–9735.Google ScholarCross Ref
- [70] . 2016. Minimizing inter-server communications by exploiting self-similarity in online social networks. IEEE Trans. Parallel Distrib. Syst. 27, 4 (2016), 1116–1130.Google ScholarDigital Library
Index Terms
- Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
Recommendations
User Popularity Preference Aware Sequential Recommendation
Computational Science – ICCS 2023AbstractIn recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. ...
Dynamic time-aware collaborative sequential recommendation with attention-based network
AbstractA natural way of user modeling in sequential recommendation is to capture long-term and short-term preferences, respectively, given user historical behaviors and then fuse them together. Most existing approaches building on attention-based network ...
Knowledge-enhanced personalized hierarchical attention network for sequential recommendation
AbstractSequential recommendation aims to predict the next items that users will interact with according to the sequential dependencies within historical user interactions. Recently, self-attention based sequence modeling methods have become the ...
Comments