Skip to main content
SpringerLink
Log in

Recommendation over time: a probabilistic model of time-aware recommender systems

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In time-aware recommender systems, we have to consider the dynamic aspect of recommendation that is fond of new coming data. Usually, the recent data is more closely related to current recommendation tasks and the early data are useful to indicate overall measurements of the preferences. We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering systems. Our model is dynamic in the sense that it updates every time receiving new data. The time cost of every updating has a constant limit, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.

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

Similar content being viewed by others

References

  1. Koren Y. Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. 447–456

  2. Baltrunas L, Amatriain X. Towards time-dependant recommendation based on implicit feedback. In: Proceedings of Workshop on Context-aware Recommender Systems, New York, 2009. 423–424

  3. Xiang L, Yang Q. Time-dependent models in collaborative filtering based recommender system. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Milan, 2009. 450–457

  4. Agarwal D, Chen B C, Elango P. Fast online learning through offline initialization for time-sensitive recommendation. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. 703–712

  5. Koenigstein N, Dror G, Koren Y. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: Proceedings of the 5th ACM Conference on Recommender Systems, 2011. 165–172

  6. Ramirez-Garcia X, Garcia-Valdez M. A pre-filtering based context-aware recommender system using fuzzy rules. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Berlin: Springer, 2015. 497–505

    Chapter  Google Scholar 

  7. Suksawatchon J, Darapisut S, Suksawatchon U. The constant time of predictive algorithm for music recommendation with time context. In: Proceedings of International Joint Conference on Computer Science and Software Engineering, 2015. 63–68

  8. Bogina V, Kuflik T, Mokryn O. Learning item temporal dynamics for predicting buying sessions. In: Proceedings of the 21st Annual Meeting of the Intelligent Interfaces, Sonoma, 2016. 251–255

  9. Xiong L, Chen X, Huang T K, et al. Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: Proceedings of the SIAM International Conference on Data Mining, 2010. 211–222

  10. Dunlavy D M, Kolda T G, Acar E. Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data, 2011, 5: 1–27

    Article  Google Scholar 

  11. Bhargava P, Phan T, Zhou J Y, et al. Who, what, when, and where: multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In: Proceedings of International World Wide Web Conferences Steering Committee, 2015. 130–140

  12. Géry M, Haddad H. Evaluation of web usage mining approaches for user’s next request prediction. In: Proceedings of the 5th ACM International Workshop on Web Information and Data Management, New Orleans, 2003. 74–81

  13. Huang Y M, Huang T C, Wang K T, et al. A Markov-based recommendation model for exploring the transfer of learning on the web. J Educ Tech Soc, 2009, 12: 144–162

    Google Scholar 

  14. Awad M A, Khalil I. Prediction of user’s web-browsing behavior: application of Markov model. IEEE Trans Syst Man Cybern B, 2012, 42: 1131–1142

    Article  Google Scholar 

  15. Hariri N, Mobasher B, Burke R. Context-aware music recommendation based on latenttopic sequential patterns. In: Proceedings of ACM Conference on Recommender Systems, 2012. 131–138

  16. Chen W, Niu Z D, Zhao X Y, et al. A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 2014, 17: 271–284

    Article  Google Scholar 

  17. Zhang J D, Chow C Y. Point-of-interest recommendations in location-based social networks. SIGSPATIAL Special, 2016, 7: 26–33

    Article  Google Scholar 

  18. Chen J, Wang C K, Wang J M. A personalized interest-forgetting Markov model for recommendations. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015. 16–22

  19. Gopalachari M V, Sammulal P. Hybrid recommender system with conceptualization and temporal preferences. In: Proceedings of the 2nd International Conference on Computer and Communication Technologies, 2015. 811–819

  20. Sahoo N, Singh P V, Mukhopadhyay T. A hidden Markov model for collaborative filtering. Mis Quart, 2012, 36: 1329–1356

    Article  Google Scholar 

  21. Sanchez F, Alduan M, Alvarez F, et al. Recommender system for sport videos based on user audiovisual consumption. IEEE Trans Multimedia, 2012, 14: 1546–1557

    Article  Google Scholar 

  22. Alanazi A, Bain M. A people-to-people content-based reciprocal recommender using hidden markov models. In: Proceedings of the 7th ACM Conference on Recommender Systems, 2013. 303–306

  23. Gu W R, Dong S B, Zeng Z Z. Increasing recommended effectiveness with markov chains and purchase intervals. Neural Comput Applic, 2014, 25: 1153–1162

    Article  Google Scholar 

  24. Zhang H D, Ni W C, Li X, et al. Modeling the heterogeneous duration of user interest in time-dependent recommendation: a hidden semi-Markov approach. IEEE Trans Syst Man Cybern Syst, 2018, 48: 177–194

    Article  Google Scholar 

  25. Zhang H, Ni W, Li X, et al. A hidden semi-Markov approach for time-dependent recommendation. In: Proceedings of Pacific Asia Conference on Information Systems, 2016

  26. Le D T, Fang Y, Lauw H W. Modeling sequential preferences with dynamic user and context factors. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Riva del Garda, 2016. 145–161

  27. Alanazi A, Bain M. A scalable people-to-people hybrid reciprocal recommender using hidden Markov models. In: Proceedings of the 2nd International Workshop on Machine Learning Methods for Recommender Systems, 2016

  28. Lu Z, Agarwal D, Dhillon I S. A spatio-temporal approach to collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems, New York, 2009. 13–20

  29. Paisley J, Gerrish S, Blei D. Dynamic modeling with the collaborative Kalman filter. In: Proceedings of the 5th Annual NYAS Machine Learning Symposium, 2010

  30. Sun J Z, Varshney K R, Subbian K. Dynamic matrix factorization: a state space approach. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2012. 1897–1900

  31. Sun J Z, Parthasarathy D, Varshney K R. Collaborative Kalman filtering for dynamic matrix factorization. IEEE Trans Signal Process, 2014, 62: 3499–3509

    Article  MathSciNet  Google Scholar 

  32. Gultekin S, Paisley J. A collaborative Kalman filter for time-evolving dyadic processes. In: Proceedings of IEEE International Conference on Data Mining, Shenzhen, 2014. 140–149

  33. Ding Y, Li X. Time weight collaborative filtering. In: Proceedings of the ACM International Conference on Information and Knowledge Management, 2005. 485–492

  34. Liu N N, Zhao M, Xiang E, et al. Online evolutionary collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, 2010. 95–102

  35. Chandramouli B, Levandoski J J, Eldawy A, et al. StreamRec: a real-time recommender system. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, 2011. 6–8

  36. Huang Y X, Cui B, Zhang W Y, et al. TencentRec: real-time stream recommendation in practice. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015. 227–238

  37. Su H Y, Lin X F, Yan B, et al. The collaborative filtering algorithm with time weight based on mapReduce. In: Proceedings of International Conference on Big Data Computing and Communications, 2015. 386–395

  38. Rezaeimehr F, Moradi P, Ahmadian S, et al. TCARS: time- and community-aware recommendation system. Future Generation Comput Syst, 2018, 78: 419–429

    Article  Google Scholar 

  39. Yu H, Li Z Y. A collaborative filtering method based on the forgetting curve. In: Proceedings of the 2010 International Conference on Web Information Systems and Mining, Sanya, 2010. 183–187

  40. Shi Y C. An improved collaborative filtering recommendation method based on timestamp. In: Proceedings of International Conference on Advanced Communication Technology, 2014. 1180–1184

  41. Xiang L, Yuan Q, Zhao S W, et al. Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. 723–731

  42. Ding Y, Wang D, Li G Q, et al. Exploiting long-term and short-term preferences and RFID trajectories in shop recommendation. Softw Pract Exper, 2017, 47: 849–865

    Article  Google Scholar 

  43. Yu J, Liu F F. A short-term user interest model for personalized recommendation. In: Proceedings of IEEE International Conference on Information Management and Engineering, Chengdu, 2010. 219–222

  44. Li L, Zheng L, Yang F, et al. Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst Appl, 2014, 41: 3168–3177

    Article  Google Scholar 

  45. Basile P, Caputo A, Gemmis M D, et al. Modeling short-term preferences in time-aware recommender systems. In: Proceedings of Workshop on Deep Content Analytics Techniques for Personalized and Intelligent Services, 2015. 44–54

  46. Daneshmand S M, Javari A, Abtahi S E, et al. A time-aware recommender system based on dependency network of items. Comput J, 2015, 58: 1955–1966

    Article  Google Scholar 

  47. Azadjalal M M, Moradi P, Abdollahpouri A, et al. A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Syst, 2017, 116: 130–143

    Article  Google Scholar 

  48. Chang S Y, Zhang Y, Tang J L, et al. Streaming recommender systems. In: Proceedings of the 26th International Conference on World Wide Web, Perth, 2017. 381–389

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

  50. Devooght R, Bersini H. Long and short-term recommendations with recurrent neural networks. In: Proceedings of Conference on User Modeling, Adaptation and Personalization, 2017. 13–21

  51. Wu C Y, Ahmed A, Beutel A, et al. Recurrent recommender networks. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, 2017. 495–503

  52. Villatel K, Smirnova E, Mary J, et al. Recurrent neural networks for long and short-term sequential recommendation. 2018. ArXiv: 1807.09142

  53. Hidasi B, Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, 2018. 843–852

  54. Durrett R. Essentials of Stochastic Processes. New York: Springer, 2012. 139–183

    Book  Google Scholar 

  55. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE, 1989, 77: 257–286

    Article  Google Scholar 

  56. Harper F M, Konstan J A. The MovieLens datasets. ACM Trans Interact Intell Syst, 2016, 5: 1–19

    Article  Google Scholar 

  57. Tang J L, Gao H J, Liu H. Mtrust: discerning multi-faceted trust in a connected world. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 2012. 93–102

  58. Tang J L, Liu H, Gao H J, et al. Etrust: understanding trust evolution in an online world. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012. 253–261

  59. Massa P, Avesani P. Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, New York, 2007. 17–24

  60. Jalili M, Ahmadian S, Izadi M, et al. Evaluating collaborative filtering recommender algorithms: a survey. IEEE Access, 2018, 6: 74003–74024

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61672049, 61732001).

Author information

Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Zuoquan Lin & Hanxuan Chen

Authors
  1. Zuoquan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanxuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zuoquan Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, Z., Chen, H. Recommendation over time: a probabilistic model of time-aware recommender systems. Sci. China Inf. Sci. 62, 212105 (2019). https://doi.org/10.1007/s11432-018-9915-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9915-8

Keywords

Search

Navigation