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Time-aware sequence model for next-item recommendation

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Abstract

The sequences of users’ behaviors generally indicate their preferences, and they can be used to improve next-item prediction in sequential recommendation. Unfortunately, users’ behaviors may change over time, making it difficult to capture users’ dynamic preferences directly from recent sequences of behaviors. Traditional methods such as Markov Chains (MC), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks only consider the relative order of items in a sequence and ignore important time information such as the time interval and duration in the sequence. In this paper, we propose a novel sequential recommendation model, named Interval- and Duration-aware LSTM with Embedding layer and Coupled input and forget gate (IDLSTM-EC), which leverages time interval and duration information to accurately capture users’ long-term and short-term preferences. In particular, the model incorporates global context information about sequences in the input layer to make better use of long-term memory. Furthermore, the model introduces the coupled input and forget gate and embedding layer to further improve efficiency and effectiveness. Experiments on real-world datasets show that the proposed approaches outperform the state-of-the-art baselines and can handle the problem of data sparsity effectively.

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Notes

  1. http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html

  2. https://github.com/vallzey/IDLSTM

References

  1. Dakhel AM, Malazi HT, Mahdavi M (2018) A social recommender system using item asymmetric correlation. Appl Intell 48(3):527–540

    Article  Google Scholar 

  2. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  3. Eppler MJ, Mengis J (2004) The concept of information overload: a review of literature from organization science, accounting, marketing, mis, and related disciplines. The Information Society 20(5):325–344

    Article  Google Scholar 

  4. Fu C (2020) User correlation model for question recommendation in community question answering. Appl Intell 50(2):634–645

    Article  Google Scholar 

  5. Garcin FF, Dimitrakakis C, Faltings B (2013) Personalized news recommendation with context trees. In: 7Th ACM recommender systems conference (recsys 2013), CONF

  6. Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) Lstm:, A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems 28(10):2222–2232

    Article  MathSciNet  Google Scholar 

  7. Guan Y, Wei Q, Chen G (2019) Deep learning based personalized recommendation with multi-view information integration. Decis Support Syst 118:58–69

    Article  Google Scholar 

  8. Gupta S, Varma V (2017) Scientific article recommendation by using distributed representations of text and graph. In: Proceedings of the 26th international conference on world wide Web companion, International World Wide Web Conferences Steering Committee, pp 1267–1268

  9. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: 4Th international conference on learning representations, ICLR 2016

  10. Hidasi B, Tikk D (2016) General factorization framework for context-aware recommendations. Data Min Knowl Disc 30(2):342–371

    Article  MathSciNet  Google Scholar 

  11. Hu L, Chen Q, Zhao H, Jian S, Cao L, Cao J (2018) Neural cross-session filtering: Next-item prediction under intra-and inter-session context. IEEE Intell Syst 33(6):57–67

    Article  Google Scholar 

  12. Linden G, Smith B, York J (2003) Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1):76–80

    Article  Google Scholar 

  13. Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining, ACM, pp 287–296

  14. Nisha C, Mohan A (2019) A social recommender system using deep architecture and network embedding. Appl Intell 49(5):1937–1953

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web: methods and strategies of Web personalization. Springer, Berlin, pp 325–341

  17. Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  18. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, AUAI Press, pp 452–461

  19. 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, ACM, pp 811–820

  20. Shi H, Chen L, Xu Z, Lyu D (2019) Personalized location recommendation using mobile phone usage information. Appl Intell 49(10):3694–3707

    Article  Google Scholar 

  21. Twardowski B (2016) Modelling contextual information in session-aware recommender systems with neural networks. In: Proceedings of the 10th ACM conference on recommender systems, ACM, pp 273–276

  22. Wang D, Deng S, Xu G (2018) Sequence-based context-aware music recommendation. Information Retrieval Journal 21(2-3):230–252

    Article  Google Scholar 

  23. Wang D, Deng S, Zhang X, Xu G (2018) Learning to embed music and metadata for context-aware music recommendation. World Wide Web 21(5):1399–1423

    Article  Google Scholar 

  24. Wang D, Zhang X, Yu D, Xu G, Deng S (2020) Came: Content-and context-aware music embedding for recommendation. IEEE Transactions on Neural Networks and Learning Systems

  25. Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (2015) Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 403–412

  26. Wang S, Hu L, Cao L, Huang X, Lian D, Liu W (2018) Attention-based transactional context embedding for next-item recommendation. In: Thirty-second AAAI conference on artificial intelligence

  27. Wang S, Hu L, Wang Y, Sheng QZ, Orgun M, Cao L (2019) Modeling multi-purpose sessions for nextitem recommendations via mixture-channel purpose routing networks. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI Press, pp 1–7

  28. Wu Y, Li K, Zhao G, Qian X (2019) Long-and short-term preference learning for next poi recommendation. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 2301–2304

  29. Xiang L, Yuan Q, Zhao S, Chen L, Zhang X, Yang Q, Sun J (2010) 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, pp 723–732

  30. Xiao T, Shen H (2019) Neural variational matrix factorization for collaborative filtering in recommendation systems. Appl Intell 49(10):3558–3569

    Article  Google Scholar 

  31. Xing S, Wang Q, Zhao X, Li T, et al. (2019) Content-aware point-of-interest recommendation based on convolutional neural network. Appl Intell 49(3):858–871

    Article  Google Scholar 

  32. Yap GE, Li XL, Philip SY (2012) Effective next-items recommendation via personalized sequential pattern mining. In: International conference on database systems for advanced applications, Springer, pp 48–64

  33. Yu D, Xu K, Wang D, Yu T, Li W (2019) Point-of-interest recommendation based on user contextual behavior semantics. Int J Softw Eng Knowl Eng 29(11n12):1781–1799

    Article  Google Scholar 

  34. Yu Z, Lian J, Mahmoody A, Liu G, Xie X (2019) Adaptive user modeling with long and short-term preferences for personalized recommendation. In: Proceedings of the 28th international joint conference on artificial intelligence, AAAI Press, pp 4213–4219

  35. Zhang Y, Dai H, Xu C, Feng J, Wang T, Bian J, Wang B, Liu TY (2014) Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of the Twenty-Eighth AAAI conference on artificial intelligence, AAAI’14, AAAI Press, pp 1369–1375

  36. Zhao G, Liu Z, Chao Y, Qian X (2020) Caper: Context-aware personalized emoji recommendation. IEEE Transactions on Knowledge and Data Engineering

  37. Zhu Y, Li H, Liao Y, Wang B, Guan Z, Liu H, Cai D (2017) What to do next: modeling user behaviors by time-lstm. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, AAAI Press, pp 3602–3608

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Dongjing Wang, Dengwei Xu & Dongjin Yu

  2. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

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  1. Dongjing Wang
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  2. Dengwei Xu
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  3. Dongjin Yu
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  4. Guandong Xu
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Correspondence to Dongjin Yu.

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This research was supported by Zhejiang Provincial Natural Science Foundation of China under No. LQ20F020015, and the Fundamental Research Funds for the Provincial University of Zhejiang by Hangzhou Dianzi University under No. GK199900299012-017.

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Wang, D., Xu, D., Yu, D. et al. Time-aware sequence model for next-item recommendation. Appl Intell 51, 906–920 (2021). https://doi.org/10.1007/s10489-020-01820-2

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