Top

World Wide Web

Published in:

01-01-2024

Knowledge-enhanced personalized hierarchical attention network for sequential recommendation

Authors: Shuqi Ruan, Chao Yang, Dongsheng Li

Published in: World Wide Web | Issue 1/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Sequential 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 mainstream method due to their competitive accuracy. Despite their effectiveness, these methods still have non-trivial limitations: (1) they mainly take the transition patterns between items into consideration but ignore the semantic associations between items, and (2) they mostly focus on dynamic short-term user preferences and fail to consider user static long-term preferences explicitly. To address these limitations, we propose a Knowledge Enhanced Personalized Hierarchical Attention Network (KPHAN), which can incorporate the semantic associations among items by learning from knowledge graphs and capture the fine-grained long- and short-term interests of users through a novel personalized hierarchical attention network. Specifically, we employ the entities and relationships in the knowledge graph to enrich semantic information for items while preserving the structural information of the knowledge graph. The self-attention mechanism then captures semantic associations among items to obtain short-term user preferences more accurately. Finally, a personalized hierarchical attention network is developed to generate the final user preference representations, which can fully capture user static long-term preferences while fusing dynamic short-term preferences. Experimental results on three real-world datasets demonstrate that our method can outperform prior works by 2.7% - 35.5% on HR metrics and 6.7% - 27.9% on NDCG metrics.

previous article Privacy-preserving data publishing: an information-driven distributed genetic algorithm

next article A novel robust memetic algorithm for dynamic community structures detection in complex networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

Smith, B., Linden, G.: Two decades of recommender systems at amazon. com. IEEE Internet Comput. 21(3), 12–18 (2017)

Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269–1278 (2019)

Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings (2016)

Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)

Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., Orgun, M.: Sequential recommender systems: challenges, progress and prospects. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp. 6332–6338 (2019)

Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)

Fang, H., Zhang, D., Shu, Y., Guo, G.: Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS) 39(1), 1–42 (2020)

He, R., McAuley, J.: Fusing similarity models with markov chains for sparse sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 191–200. IEEE (2016)

10.

Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659–667 (2013)

11.

Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)

12.

Tuan, T.X., Phuong, T.M.: 3d convolutional networks for session-based recommendation with content features. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 138–146 (2017)

13.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)

14.

Zhang, T., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Wang, D., Liu, G., Zhou, X.: Feature-level deeper self-attention network for sequential recommendation. In: IJCAI, pp. 4320–4326 (2019)

15.

Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)

16.

Cho, S.M., Park, E., Yoo, S.: Meantime: Mixture of attention mechanisms with multi-temporal embeddings for sequential recommendation. In: Fourteenth ACM Conference on Recommender Systems, pp. 515–520 (2020)

17.

Zheng, L., Guo, N., Chen, W., Yu, J., Jiang, D.: Sentiment-guided sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1957–1960 (2020)

18.

Wu, L., Li, S., Hsieh, C.-J., Sharpnack, J.: Sse-pt: Sequential recommendation via personalized transformer. In: Fourteenth ACM Conference on Recommender Systems, pp. 328–337 (2020)

19.

Yuan, X., Duan, D., Tong, L., Shi, L., Zhang, C.: Icai-sr: Item categorical attribute integrated sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1687–1691 (2021)

20.

Xie, Y., Zhou, P., Kim, S.: Decoupled side information fusion for sequential recommendation. Proceedings of the 45th International ACM SIGIR conference on Research and Development in Information Retrieval (2022)

21.

Cai, W., Pan, W., Mao, J., Yu, Z., Xu, C.: Aspect re-distribution for learning better item embeddings in sequential recommendation. In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 49–58 (2022)

22.

Rashed, A., Elsayed, S., Schmidt-Thieme, L.: Context and attribute-aware sequential recommendation via cross-attention. In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 71–80 (2022)

23.

Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)

24.

Wang, J., Ding, K., Hong, L., Liu, H., Caverlee, J.: Next-item recommendation with sequential hypergraphs. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1101–1110 (2020)

25.

Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., Jin, D., Li, Y.: Sequential recommendation with graph neural networks. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 378–387 (2021)

26.

Pang, Y., Wu, L., Shen, Q., Zhang, Y., Wei, Z., Xu, F., Chang, E., Long, B., Pei, J.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 775–783 (2022)

27.

Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. 34(8), 3549–3568 (2020)

28.

Wang, H., Zhang, F., Xie, X., Guo, M.: Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 1835–1844 (2018)

29.

Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.-Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)

30.

Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.-S.: Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5329–5336 (2019)

31.

Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y., Xu, C.: Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 548–556 (2019)

32.

Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., Guo, M.: Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417–426 (2018)

33.

Wang, X., He, X., Cao, Y., Liu, M., Chua, T.-S.: Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950–958 (2019)

34.

Huang, J., Zhao, W.X., Dou, H., Wen, J.-R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 505–514 (2018)

35.

Wang, P., Fan, Y., Xia, L., Zhao, W.X., Niu, S., Huang, J.: Kerl: A knowledge-guided reinforcement learning model for sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 209–218 (2020)

36.

Shani, G., Heckerman, D., Brafman, R.I., Boutilier, C.: An MDP-based recommender system. Journal of Machine Learning Research 6(9) (2005)

37.

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

38.

Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248 (2016)

39.

Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 495–503 (2017)

40.

Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)

41.

Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137 (2017)

42.

Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22 (2016)

43.

Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 729–732 (2016)

44.

Li, J., Wang, Y., McAuley, J.: Time interval aware self-attention for sequential recommendation. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 322–330 (2020)

45.

Lin, J., Pan, W., Ming, Z.: Fissa: fusing item similarity models with self-attention networks for sequential recommendation. In: Fourteenth ACM Conference on Recommender Systems, pp. 130–139 (2020)

46.

Xu, C., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Zhuang, F., Fang, J., Zhou, X.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol. 19, pp. 3940–3946 (2019)

47.

Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 26 (2013)

48.

Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)

49.

Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (volume 1: Long Papers), pp. 687–696 (2015)

50.

Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI Conference on Artificial Intelligence (2015)

51.

Zhao, W.X., He, G., Yang, K., Dou, H., Huang, J., Ouyang, S., Wen, J.-R.: Kb4rec: A data set for linking knowledge bases with recommender systems. Data Intell. 1(2), 121–136 (2019)

52.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. the 3rd International Conference for Learning Representations (2015)

53.

Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS) 5(4), 1–19 (2015)

54.

Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)

55.

Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)

56.

He, R., Kang, W.-C., McAuley, J.: Translation-based recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 161–169 (2017)

57.

Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 825–833 (2019)

Title: Knowledge-enhanced personalized hierarchical attention network for sequential recommendation
Authors: Shuqi Ruan
Chao Yang
Dongsheng Li
Publication date: 01-01-2024
Publisher: Springer US
Published in: World Wide Web / Issue 1/2024
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-024-01236-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 1/2024

Rumor blocking with pertinence set in large graphs

Discrete cross-modal hashing with relaxation and label semantic guidance

Efficient approximation and privacy preservation algorithms for real time online evolving data streams

Adaptive retrofitting for industrial machines: utilizing webassembly and peer-to-peer connectivity on the edge

Privacy-preserving data publishing: an information-driven distributed genetic algorithm

A novel robust memetic algorithm for dynamic community structures detection in complex networks

Premium Partner