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International Journal of Data Science and Analytics

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29-06-2023 | Editorial

Data science for next-generation recommender systems

Authors: Shoujin Wang, Yan Wang, Fikret Sivrikaya, Sahin Albayrak, Vito Walter Anelli

Published in: International Journal of Data Science and Analytics | Issue 2/2023

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Abstract

Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.

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Wang, S., Pasi, G., Hu, L., Cao, L.: The era of intelligent recommendation: editorial on intelligent recommendation with advanced ai and learning. IEEE Intell. Syst. 35(5), 3–6 (2020)CrossRef

Wang, S., Zhang, X., Wang, Y., Liu, H., Ricci, F.: Trustworthy recommender systems. arXiv preprint http://arxiv.org/abs/2208.06265 (2022)

Wang, S., Liu, N., Zhang, X., Wang, Y., Ricci, F., Mobasher, B.: Data science and artificial intelligence for responsible recommendations. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4904–4905 (2022)

Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRef

Rong, X.: word2vec parameter learning explained. arXiv preprint http://arxiv.org/abs/1411.2738 (2014)

Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based transactional context embedding for next-item recommendation. In: 32nd AAAI Conference on Artificial Intelligence, pp. 2532–2539 (2018)

Wang, S., Hu, L., Cao, L.: Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 285–302 (2017). Springer

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26 (2013)

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint http://arxiv.org/abs/1301.3781 (2013)

10.

Lindeberg, T.: Scale invariant feature transform (2012)

11.

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005). IEEE

12.

O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint http://arxiv.org/abs/1511.08458 (2015)

13.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for largescale image recognition. arXiv preprint http://arxiv.org/abs/1409.1556 (2014)

14.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

15.

Wang, S., Hu, L., Wang, Y., Cao, L., Sheng, Q.Z., Orgun, M.: Sequential recommender systems: challenges, progress and prospects. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 6332–6338 (2019)

16.

Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. (CSUR) 54(7), 1–38 (2021)CrossRef

17.

Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Modeling multi-purpose sessions for next-item recommendations via mixturechannel purpose routing networks. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3771–3777 (2019). AAAI Press

18.

Song, W., Wang, S., Wang, Y., Wang, S.: Next-item recommendations in short sessions. In: Proceedings of the 15th ACM Conference on Recommender Systems, pp. 282–291 (2021)

19.

Wang, N., Wang, S., Wang, Y., Sheng, Q.Z., Orgun, M.A.: Exploiting intra-and inter-session dependencies for session-based recommendations. World Wide Web 25(1), 425–443 (2022)CrossRef

20.

Wang, X., Sun, G., Fang, X., Yang, J., Wang, S.: Modeling spatiotemporal neighbourhood for personalized point-of-interest recommendation. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence, pp. 3530–3536 (2022)

21.

Wang, S., Xu, X., Zhang, X., Wang, Y., Song, W.: Veracity-aware and event-driven personalized news recommendation for fake news mitigation. In: Proceedings of the ACM Web Conference 2022, pp. 3673–3684 (2022)

22.

Wang, R., Wang, S., Lu, W., Peng, X.: News recommendation via multi-interest news sequence modelling. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7942–7946 (2022). IEEE

23.

Wang, R., Wang, S., Lu, W., Peng, X., Zhang, W., Zheng, C., Qiao, X.: Intention-aware user modeling for personalized news recommendation. In: 28th International Conference on Database Systems for Advanced Applications, pp. 179–194 (2023)

24.

Guo, W., Wang, S., Lu, W., Wu, H., Zhang, Q., Shao, Z.: Sequential dependency enhanced graph neural networks for session-based recommendations. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10 (2021). IEEE

25.

Wang, N., Wang, S., Wang, Y., Sheng, Q.Z., Orgun, M.: Modelling local and global dependencies for next-item recommendations. In: International Conference on Web Information Systems Engineering, pp. 285–300 (2020). Springer

26.

Zhang, Q., Wang, S., Lu, W., Feng, C., Peng, X., Wang, Q.: Rethinking adjacent dependency in session-based recommendations. In: Advances in Knowledge Discovery and Data Mining: Part III, pp. 301–313 (2022). Springer

27.

Ye, R., Zhang, Q., Luo, H.: Cross-session aware temporal convolutional network for session-based recommendation. In: 2020 International Conference on Data Mining Workshops, pp. 220–226 (2020). IEEE

28.

Wang, S., Zhang, Q., Hu, L., Zhang, X., Wang, Y., Aggarwal, C.: Sequential/session-based recommendations: Challenges, approaches, applications and opportunities. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3425–3428 (2022)

29.

Wang, S., Hu, L., Wang, Y., He, X., Sheng, Q.Z., Orgun, M.A., Cao, L., Ricci, F., Yu, P.S.: Graph learning based recommender systems: a review. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, pp. 4644–4652 (2021). AAAI Press

30.

Wang, S., Cao, L.: Inferring implicit rules by learning explicit and hidden item dependency. IEEE Trans. Syst. Man Cybern. Syst. 50(3), 935–946 (2020)CrossRef

31.

Han, E.-H., Karypis, G., Kumar, V.: Scalable parallel data mining for association rules. IEEE Trans. Knowl. Data Eng. 12(3), 337–352 (2000)CrossRef

32.

Mobasher, B., et al.: Effective personalization based on association rule discovery from web usage data. In: WIDM, pp. 9–15 (2001). ACM

33.

Forsati, R., Meybodi, M., Neiat, A.G.: Web page personalization based on weighted association rules. In: ICECT, pp. 130–135 (2009). IEEE

34.

Yap, G.-E., Li, X.-L., Philip, S.Y.: Effective next-items recommendation via personalized sequential pattern mining. In: DASFAA, pp. 48–64 (2012). Springer

35.

Song, W., Yang, K.: Personalized recommendation based on weighted sequence similarity. In: Practical Applications of Intelligent Systems, pp. 657–666 (2014)

36.

Koren, Y., Rendle, S., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 91–142 (2021)

37.

Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: WWW, pp. 811–820 (2010). ACM

38.

Yu, J., Yin, H., Li, J., Gao, M., Huang, Z., Cui, L.: Enhancing social recommendation with adversarial graph convolutional networks. IEEE Trans. Knowl. Data Eng. 34(8), 3727–3739 (2020)CrossRef

39.

Huang, C., Wang, S., Wang, X., Yao, L.: Modeling temporal positive and negative excitation for sequential recommendation. In: Proceedings of the ACM Web Conference 2023, pp. 1252–1263 (2023)

40.

Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 1–38 (2019)CrossRef

41.

Zhu, F., Chen, C., Wang, Y., Liu, G., Zheng, X.: Dtcdr: a framework for dual-target cross-domain recommendation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1533–1542 (2019)

42.

Wu, C., Yan, M.: Session-aware information embedding for e-commerce product recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2379–2382 (2017)

43.

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

44.

Zhu, F., Wang, Y., Chen, C., Liu, G., Orgun, M., Wu, J.: A deep framework for cross-domain and cross-system recommendations. In: 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, pp. 3711–3717 (2018)

45.

Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint http://arxiv.org/abs/1511.06939 (2015)

46.

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)

47.

Jaradat, S., Dokoohaki, N., Hammar, K., Wara, U., Matskin, M.: Dynamic CNN models for fashion recommendation in instagram. In: 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications, pp. 1144–1151 (2018)

48.

Deldjoo, Y., Nazary, F., Ramisa, A., Mcauley, J., Pellegrini, G., Bellogin, A., Di Noia, T.: A review of modern fashion recommender systems. arXiv preprint http://arxiv.org/abs/2202.02757 (2022)

49.

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)

50.

Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426 (2019)

51.

Zhu, F., Wang, Y., Chen, C., Liu, G., Zheng, X.: A graphical and attentional framework for dual-target cross-domain recommendation. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 3001–3008 (2021)

52.

He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: 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, pp. 639–648 (2020)

53.

Liu, Q., Xie, R., Chen, L., Liu, S., Tu, K., Cui, P., Zhang, B., Lin, L.: Graph neural network for tag ranking in tag-enhanced video recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2613–2620 (2020)

54.

Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 1–37 (2022)CrossRef

55.

Zhao, Y., Wang, S., Wang, Y., Liu, H.: Mbsrs: a multi-behavior streaming recommender system. Inf. Sci. 631, 145–163 (2023)CrossRef

56.

Shao, Z., Wang, S., Zhang, Q., Lu, W., Li, Z., Peng, X.: A systematical evaluation for next-basket recommendation algorithms. arXiv preprint http://arxiv.org/abs/2209.02892 (2022)

57.

Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Intention nets: psychology-inspired user choice behavior modeling for next-basket prediction. In: 34th AAAI Conference on Artificial Intelligence, pp. 6259–6266 (2020)

58.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

59.

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)

60.

Chen, Q., Zhao, H., Li, W., Huang, P., Ou, W.: Behavior sequence transformer for e-commerce recommendation in alibaba. In: Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, pp. 1–4 (2019)

61.

Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 299–315 (2022)

62.

Dai, S., Shao, N., Zhao, H., Yu, W., Si, Z., Xu, C., Sun, Z., Zhang, X., Xu, J.: Uncovering chatgpt’s capabilities in recommender systems. arXiv preprint http://arxiv.org/abs/2305.02182 (2023)

63.

Liu, J., Liu, C., Lv, R., Zhou, K., Zhang, Y.: Is chatgpt a good recommender? A preliminary study. arXiv preprint http://arxiv.org/abs/2304.10149 (2023)

64.

Zhang, J., Bao, K., Zhang, Y., Wang, W., Feng, F., He, X.: Is chatgpt fair for recommendation? Evaluating fairness in large language model recommendation. arXiv preprint http://arxiv.org/abs/2305.07609 (2023)

65.

Lu, X., Tsao, Y., Matsuda, S., Hori, C.: Speech enhancement based on deep denoising autoencoder. In: Interspeech, vol. 2013, pp. 436–440 (2013)

66.

Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)

67.

Ma, J., Zhou, C., Cui, P., Yang, H., Zhu, W.: Learning disentangled representations for recommendation. Adv. Neural Inf. Process. Syst. 32 (2019)

68.

Balog, K., Radlinski, F., Arakelyan, S.: Transparent, scrutable and explainable user models for personalized recommendation. In: Proceedings of the 42nd International Acm Sigir Conference on Research and Development in Information Retrieval, pp. 265–274 (2019)

69.

Wu, P., Li, H., Deng, Y., Hu, W., et al.: On the opportunity of causal learning in recommendation systems: foundation, estimation, prediction and challenges. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 23–29 (2022)

70.

Song, W., Wang, S., Wang, Y., Liu, K., Liu, X., Yin, M.: A counterfactual collaborative session-based recommender system. In: Proceedings of the ACM Web Conference 2023, pp. 971–982 (2023)

71.

Sivrikaya, F., Albayrak, S., Lian, D.: International workshop on model selection and parameter tuning in recommender systems. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2999–3000 (2019)

72.

Liu, Y., Zhang, H., Sun, Z., et al.: Wsdm 2023 workshop on interactive recommender systems. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 1275–1276 (2023)

73.

Fionda, V., Hartig, O., Abdolazimi, R., Amer-Yahia, S., Chen, H., Chen, X., Cui, P., Dalton, J., Dong, X.L., Espin-Noboa, L., et al.: Tutorials at the web conference 2023. In: Companion Proceedings of the ACM Web Conference 2023, pp. 648–658 (2023)

74.

Kelen, D.M., Benczur, A.A.: A probabilistic perspective on nearest neighbor for implicit recommendation. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

75.

Zhou, D., Zhang, Z., Zheng, Y., Zou, Z., Zheng, L.: Attenuated sentimentaware sequential recommendation. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

76.

Hong, M., Chung, N., Koo, C., Koh, S.-Y.: Tpedtr: temporal preference embedding-based deep tourism recommendation with card transaction data. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

77.

Laroussi, C., Ayachi, R.: A deep meta-level spatio-categorical poi recommender system. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

78.

Lysenko, A., Shikov, E., Bochenina, K.: Combination of individual and group patterns for time-sensitive purchase recommendation. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

79.

Desai, M., Mehta, R.G., Rana, D.P.: Scholarrec: a scholars’ recommender system that combines scholastic influence and social collaborations in academic social networks. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-generation Recommender Systems (2023)

80.

Yuan, J., Geissler, C., Shao, W., Lommatzsch, A., Jain, B.: When algorithm selection meets bi-linear learning to rank: accuracy and inference time trade off with candidates expansion. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

81.

Wang, X., Kadıoglu, S.: Modeling uncertainty to improve personalized recommendations via Bayesian deep learning. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

82.

Sun, M., Wang, A.: Privacy preserving cold-start recommendation for outof-matrix users via content baskets. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

83.

Gebremeskel, G.G., de Vries, A.P.: Pull–push: a measure of over-or underpersonalization in recommendation. Int. J. Data Sci. Anal., Special Issue on Data Science for Next-Generation Recommender Systems (2023)

Title: Data science for next-generation recommender systems
Authors: Shoujin Wang
Yan Wang
Fikret Sivrikaya
Sahin Albayrak
Vito Walter Anelli
Publication date: 29-06-2023
Publisher: Springer International Publishing
Published in: International Journal of Data Science and Analytics / Issue 2/2023
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-023-00404-w

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