ABSTRACT
Sequential recommendation systems seek to learn users' preferences to predict their next actions based on the items engaged recently. Static behavior of users requires a long time to form, but short-term interactions with items usually meet some actual needs in reality and are more variable. RNN-based models are always constrained by the strong order assumption and are hard to model the complex and changeable data flexibly. Most of the CNN-based models are limited to the fixed convolutional kernel. All these methods are suboptimal when modeling the dynamics of item-to-item transitions. It is difficult to describe the items with complex relations and extract the fine-grained user preferences from the interaction sequence. To address these issues, we propose a knowledge-aware sequential recommender with the attention-enhanced dynamic convolutional network (KAeDCN). Our model combines the dynamic convolutional network with attention mechanisms to capture changing dependencies in the sequence. Meanwhile, we enhance the representations of items with Knowledge Graph (KG) information through an information fusion module to capture the fine-grained user preferences. The experiments on four public datasets demonstrate that KAeDCN outperforms most of the state-of-the-art sequential recommenders. Furthermore, experimental results also prove that KAeDCN can enhance the representations of items effectively and improve the extractability of sequential dependencies.
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durá n, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems, Christopher J. C. Burges, Lé on Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger (Eds.). Lake Tahoe, Nevada, United States, 2787--2795. https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html Google ScholarDigital Library
- Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017. Language Modeling with Gated Convolutional Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, Sydney, NSW, Australia, 933--941. http://proceedings.mlr.press/v70/dauphin17a.html Google ScholarDigital Library
- Tim Donkers, Benedikt Loepp, and Jü rgen Ziegler. 2017. Sequential User-based Recurrent Neural Network Recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems, Paolo Cremonesi, Francesco Ricci, Shlomo Berkovsky, and Alexander Tuzhilin (Eds.). ACM, Como, Italy, 152--160. https://doi.org/10.1145/3109859.3109877 Google ScholarDigital Library
- Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, and Nguyen Quoc Viet Hung. 2019. Streaming Session-based Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, Anchorage, AK, USA, 1569--1577. Google ScholarDigital Library
- Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A Survey on Knowledge Graph-Based Recommender Systems. CoRR, Vol. abs/2003.00911 (Sept. 2020). https://arxiv.org/abs/2003.00911Google Scholar
- Ruining He and Julian J. McAuley. 2016. Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In IEEE 16th International Conference on Data Mining. IEEE Computer Society, Barcelona, Spain, 191--200.Google Scholar
- Balá zs Hidasi and Alexandros Karatzoglou. 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, Alfredo Cuzzocrea, James Allan, Norman W. Paton, Divesh Srivastava, Rakesh Agrawal, Andrei Z. Broder, Mohammed J. Zaki, K. Selcc uk Candan, Alexandros Labrinidis, Assaf Schuster, and Haixun Wang (Eds.). ACM, Torino, Italy, 843--852. https://doi.org/10.1145/3269206.3271761 Google ScholarDigital Library
- Balá zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016a. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.). San Juan, Puerto Rico. http://arxiv.org/abs/1511.06939Google Scholar
- Balá zs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016b. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, Boston, MA, USA, 241--248. https://doi.org/10.1145/2959100.2959167 Google ScholarDigital Library
- Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-Excitation Networks. In 2018 IEEE Conference on Computer Vision and Pattern Recognition,CVPR. IEEE Computer Society, Salt Lake City, UT, USA, 7132--7141. http://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.htmlGoogle Scholar
- Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Kevyn Collins-Thompson, Qiaozhu Mei, Brian D. Davison, Yiqun Liu, and Emine Yilmaz (Eds.). ACM, Ann Arbor, MI, USA, 505--514. https://doi.org/10.1145/3209978.3210017 Google ScholarDigital Library
- Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc Van Gool. 2016. Dynamic Filter Networks. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). Barcelona, Spain, 667--675. https://proceedings.neurips.cc/paper/2016/hash/8bf1211fd4b7b94528899de0a43b9fb3-Abstract.html Google ScholarDigital Library
- Wang-Cheng Kang and Julian J. McAuley. 2018. Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining, ICDM. IEEE Computer Society, Singapore, 197--206. https://doi.org/10.1109/ICDM.2018.00035Google Scholar
- Jiacheng Li, Yujie Wang, and Julian J. McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. ACM, Houston, TX, USA, 322--330. https://doi.org/10.1145/3336191.3371786 Google ScholarDigital Library
- Jingwei Ma, Jiahui Wen, Mingyang Zhong, Weitong Chen, and Xue Li. 2019. MMM: Multi-source Multi-net Micro-video Recommendation with Clustered Hidden Item Representation Learning. Data Sci. Eng., Vol. 4, 3 (2019), 240--253. https://doi.org/10.1007/s41019-019-00101--4Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, Raleigh, NC, USA, 811--820. Google ScholarDigital Library
- Leslie F Sikos and Dean Philp. 2020. Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs. Data Science and Engineering, Vol. 5, 3 (2020), 293--316.Google ScholarCross Ref
- Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2020. Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference. AAAI Press, New York, NY, USA, 214--221.Google ScholarCross Ref
- Jiaxi Tang and Ke Wang. 2018. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, Marina Del Rey, CA, USA, 565--573. Google ScholarDigital Library
- Md. Mehrab Tanjim, Hammad A. Ayyubi, and Garrison W. Cottrell. 2020. DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudré -Mauroux (Eds.). ACM, Virtual Event, Ireland, 2237--2240. https://doi.org/10.1145/3340531.3412118 Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). Long Beach, CA, USA, 5998--6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html Google ScholarDigital Library
- Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep Knowledge-Aware Network for News Recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis (Eds.). ACM, Lyon, France, 1835--1844. https://doi.org/10.1145/3178876.3186175 Google ScholarDigital Library
- Meng Wang, Mengyue Liu, Jun Liu, Sen Wang, Guodong Long, and Buyue Qian. 2017. Safe Medicine Recommendation via Medical Knowledge Graph Embedding. CoRR, Vol. abs/1710.05980 (Oct. 2017). http://arxiv.org/abs/1710.05980Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet A.orgun. 2019. Sequential Recommender Systems: Challenges, Progress and Prospects. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. ijcai.org, Macao, China, 6332--6338. https://doi.org/10.24963/ijcai.2019/883 Google ScholarCross Ref
- Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. CBAM: Convolutional Block Attention Module. In Computer Vision - ECCV 2018 - 15th European Conference (Lecture Notes in Computer Science), Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.), Vol. 11211. Springer, Munich, Germany, 3--19. https://doi.org/10.1007/978--3-030-01234--2_1Google Scholar
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent Recommender Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Maarten de Rijke, Milad Shokouhi, Andrew Tomkins, and Min Zhang (Eds.). ACM, Cambridge, United Kingdom, 495--503. https://doi.org/10.1145/3018661.3018689 Google ScholarDigital Library
- Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. 2021. Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2--9, 2021. AAAI Press, 4503--4511. https://ojs.aaai.org/index.php/AAAI/article/view/16578Google ScholarCross Ref
- Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He. 2019. A Simple Convolutional Generative Network for Next Item Recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, Melbourne, VIC, Australia, 582--590. https://doi.org/10.1145/3289600.3290975 Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). ACM, San Francisco, CA, USA, 353--362. https://doi.org/10.1145/2939672.2939673 Google ScholarDigital Library
- Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Kaiyuan Li, Yushuo Chen, Yujie Lu, Hui Wang, Changxin Tian, Xingyu Pan, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, and Ji-Rong Wen. 2020. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms. CoRR, Vol. abs/2011.01731 (Nov. 2020). https://arxiv.org/abs/2011.01731Google Scholar
Index Terms
- A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network
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