Skip to main content
SpringerLink
Log in

Sequence-aware similarity learning for next-item recommendation

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Sequence-aware next-item recommendation has recently been studied because of the noteworthy usefulness of the sequential information integrated into recommendation algorithms. Following the development thread of sequential recommendation methods, especially the factored Markov chains segment, and based on a well-designed fusing similarity model with factored high-order Markov chains (i.e., Fossil), we propose a novel and generic similarity learning framework for next-item recommendation called sequence-aware factored mixed similarity model (S-FMSM), which contains two variants with pairwise preference learning and pointwise preference learning. Unlike the baseline methods that model the general representation and the sequential representation in two divided factorization components, we use a factored mixed similarity model that unites the general similarity and the sequential relationship between two successive items for their sequential representation learning. Experiments on six datasets show that our newly introduced general similarity can notably improve the results of the recommended ranking lists. Furthermore, a study on tuning the prior trade-off parameter indicates the importance of the general similarity on different datasets. We adjust the number of latent dimensions and try different similarity measurements, which showcases that our S-FMSM is universal and gives us more insight on it.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://grouplens.org/datasets/movielens/.

  2. http://jmcauley.ucsd.edu/data/amazon/.

  3. http://csse.szu.edu.cn/staff/panwk/publications/S-FMSM/.

References

  1. Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22(1):143–177

    Article  Google Scholar 

  2. Harper F, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst 5(4):19:1–19:19

    Google Scholar 

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

  4. He R, Fang C, Wang Z, McAuley J (2016) Vista: a visually, socially, and temporally-aware model for artistic recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pp 309–316

  5. He R, Kang W-C, McAuley J (2017) Translation-based recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems, RecSys ’17, pp 161–169

  6. He R, McAuley J (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, pp 507–517

  7. 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, pp 173–182

  8. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations, ICLR ’16

  9. Kabbur S, Ning X, Karypis G (2013) 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, KDD ’13, pp 659–667

  10. 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

  11. Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pp 1419–1428

  12. Lin X, Chen H, Pei C, Sun F, Xiao X, Sun H, Zhang Y, Ou W, Jiang P (2019) A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, RecSys ’19, pp 20–28

  13. Liu M, Pan W, Liu M ,Chen Y, Peng X, Ming Z (2017) Mixed similarity learning for recommendation with implicit feedback. Knowl-Based Syst 119(C):178–185

  14. McAuley J, Targett C, Shi Q, Van Den Hengel A (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pp 43–52

  15. Pan R, Zhou Y, Cao B, Liu NN, Lukose RM, Scholz M, Yang Q (2008) One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15–19, 2008, Pisa, Italy, pp 502–511. IEEE Computer Society

  16. Pan W, Chen L (2013) GBPR: group preference based Bayesian Personalized Ranking for one-class collaborative filtering. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI ’13, pp 2691–2697

  17. Pan W, Ming Z (2017) Collaborative recommendation with multiclass preference context. IEEE Intell Syst 32(2):45–51

    Article  Google Scholar 

  18. Pasricha R, McAuley J (2018) Translation-based factorization machines for sequential recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18, pp 63–71

  19. Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, pp 39–42

  20. Pazzani M, Billsus D (2007) Content-based recommendation systems. In: The Adaptive Web, pp 325–341. Springer

  21. Quadrana M, Cremonesi P, Jannach D (2018) Sequence-aware recommender systems. ACM Comput Surv 51(4):66

    Article  Google Scholar 

  22. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18–21, 2009, pp 452–461

  23. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI ’09, pp 452–461

  24. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp 811–820

  25. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW ’01, pp 285–295

  26. Song W, Xiao Z, Wang Y, Charlin L, Zhang M, Tang J (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining, WSDM ’19, pp 555–563

  27. Tang J, Wang K (2018) Personalized top-N sequential recommendation via convolutional sequence embedding. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM ’18, pp 565–573

  28. Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (2015) Learning hierarchical representation model for next basket recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pp 403–412

  29. Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Sequential recommender system based on hierarchical attention networks. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI ’18, pp 3926–3932

  30. Zhao T, McAuley J, King I (2014) Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp 261–270

  31. Zheleva E, Guiver J, Mendes REduarda, Milić-Frayling N (2010) Statistical models of music-listening sessions in social media. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp 1019–1028

  32. Zhong L, Lin J, Pan W, Ming Z (2020) Proceedings of the sequence-aware factored mixed similarity model for next-item recommendation. In: Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, pp 181–188

  33. Zimdars A, Maxwell CD, Meek C (2001) Using temporal data for making recommendations. In: Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, UAI ’01, pp 580–588

Download references

Acknowledgements

We thank the handling editors and reviewers for their effort and constructive expert comments, and the support of National Natural Science Foundation of China Nos. 61872249 and 61836005.

Author information

Authors and Affiliations

  1. National Engineering Laboratory for Big Data System Computing Technology and College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen, People’s Republic of China

    Zhuoxin Zhan, Liulan Zhong, Jing Lin, Weike Pan & Zhong Ming

Authors
  1. Zhuoxin Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liulan Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weike Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhong Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weike Pan or Zhong Ming.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work is an extension of our previous work [32]. Compared with our previous work, we have added the following new contents in this paper, (i) we have developed an extension of S-FMSM, i.e., a pointwise preference learning method S-FMSM(poi), in Sect. 6; (ii) we have included more empirical results, i.e., an additional baseline method TransRec in Table 3, Fig. 5, and Table 5, results of methods with different numbers of latent dimensions in Fig. 7, and results with different similarity measurements in Table 6, as well as the corresponding descriptions and discussions; (iii) we have included more related works in Sect. 2; and (iv) we have made many improvements throughout the whole paper, including figure illustrations, and more discussions and analysis.

Zhuoxin Zhan and Liulan Zhong are co-first authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhan, Z., Zhong, L., Lin, J. et al. Sequence-aware similarity learning for next-item recommendation. J Supercomput 77, 7509–7534 (2021). https://doi.org/10.1007/s11227-020-03555-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03555-w

Keywords

Search

Navigation