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Enhancing session-based trip recommendations using matrix factorization: a study on algorithm efficiency and resource utilization

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Abstract

As the impact and usefulness of recommendation systems continue to grow, their importance becomes more and more pronounced. Therefore, it is crucial to design and implement recommendation systems that are both efficient and highly accurate to meet the increasing demands and expectations. This study focuses on a model awarded first place in a travel forecasting recommendation system competition. This study aims to enhance matrix factorization-based recommender systems by conducting a comprehensive analysis of various factors. This includes examining the effects of resource utilization and recurrent neural network (RNN) algorithms on session-based factorization, as well as evaluating the influence of embeddings and optimization techniques concerning their efficiency and accuracy. The gated recurrent unit (GRU) algorithm has produced more accurate results for reduced datasets than long short-term memory (LSTM). Some modifications have been made on the embedding layers, and the results have been observed. In addition, the model’s optimizer is changed, and the performance of different optimizers is evaluated. While random reduction of the dataset has led to a decrease in the success rate, methodical reduction has significantly increased the success rate. The highest and most reliable success rate (0.6654) was achieved by applying the selection method, which reduced the dataset to 1 M records from 1.5 M records. Optimizers have shown a wide range of effects on hardware.

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Data Availability

No datasets were generated or analyzed during the current study.

Notes

  1. Dataset available at https://www.bookingchallenge.com/ and github.

  2. Source code available at https://github.com/aumat2011/Code_Thesis

References

  1. Statista: Amount of Data Created and Consumed Worldwide from 2010 to 2025. Retrieved from https://www.statista.com/statistics/871513/worldwide-data-created/ (2023)

  2. Bellini V, Di Sciascio E, Donini F M, Pomo C, Ragone A, Schiavone A (2024) A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders. J Intell Inf Syst, 1–21

  3. McDonald C, Moreira G (2021) How to build a winning recommendation system, Part 1. Accessed 4 December 2023 . https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-1/

  4. Xiao W, Yao S, Wu S (2016) Improving on recommend speed of recommender systems by using expert users. In: 2016 Chinese Control and Decision Conference (CCDC), pp 2425–2430 . https://doi.org/10.1109/CCDC.2016.7531392

  5. Duan J, Zhang P-F, Qiu R, Huang Z (2023) Long short-term enhanced memory for sequential recommendation. World Wide Web 26(2):561–583. https://doi.org/10.1007/s11280-022-01056-9

    Article  Google Scholar 

  6. McDonald C, Deotte C, Moreira G, Puget J-F, Titericz G, Ak R, Oldridge E, Liu J, Schifferer B (2021) How to Build a Winning Deep Learning Powered Recommender System-Part 3. Accessed 05 December 2023 . https://developer.nvidia.com/blog/how-to-build-a-winning-deep-learning-powered-recommender-system-part-3/

  7. Anil D, Vembar A, Hiriyannaiah S, Siddesh G, Srinivasa K (2018) Performance analysis of deep learning architectures for recommendation systems. In: 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW), pp 129–136 . IEEE

  8. Li C, Ishak I, Ibrahim H, Zolkepli M, Sidi F, Li C (2023) Deep learning-based recommendation system: systematic review and classification. IEEE Access 11:113790–113835

    Article  Google Scholar 

  9. Bandyopadhyay S, Thakur S (2020) Product prediction and recommendation in e-commerce using collaborative filtering and artificial neural networks: a hybrid approach. In Intelligent computing paradigm: recent trends, pp 59–67

  10. Olmo FHD, Gaudisso E (2008) Evaluation of recommender systems: a new approach. Expert Syst Appl 35(3):790–804

    Article  Google Scholar 

  11. Fu Z, Xian Y, Zhang Y, Zhang Y (2020) Tutorial on conversational recommendation systems. In: Proceedings of the 14th ACM Conference on Recommender Systems. RecSys ’20, pp. 751–753. Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/3383313.3411548

  12. Melville P, Sindhwani V (2010) Recommender Systems. IBM T.J. Watson Research Center, New York

    Google Scholar 

  13. Shah K, Salunke A, Dongare S, Antala K (2017) Recommender systems: An overview of different approaches to recommendations. In: 2017 International Conference on Innovations in Information Embedded and Communication Systems (ICIIECS), Lonavala

  14. Bhareti K, Perera, S, Jamal S, Pallege M H, Akash V, Wiieweera S (2020) A literature review of recommendation systems. In: 2020 IEEE International Conference for Innovation in Technology (INOCON), pp. 1–7 . https://doi.org/10.1109/INOCON50539.2020.9298450

  15. Singhal A, Sinha P, Pant R (2017) Use of deep learning in modern recommendation system: A summary of recent works. arXiv preprint arXiv:1712.07525

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

    Article  Google Scholar 

  17. Liu D Z, Singh G (2016) A recurrent neural network based recommendation system. In: International Conference on Recent Trends in Engineering, Science & Technology

  18. Mu R (2018) A survey of recommender systems based on deep learning. Ieee Access 6:69009–69022

    Article  Google Scholar 

  19. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  20. Liu D Z, Singh G (2016) A recurrent neural network based recommendation system. In: International Conference on Recent Trends in Engineering, Science & Technology

  21. Anil D, Vembar A, Hiriyannaiah S, S G M, Srinivasa K G (2018) Performance analysis of deep learning architectures for recommendation systems. In: 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW), pp. 129–136 . https://doi.org/10.1109/HiPCW.2018.8634192

  22. Zhao X, Wang M, Zhao X, Li J, Zhou S, Yin D, Li Q, Tang J, Guo R (2023) Embedding in recommender systems: a survey. arXiv preprint arXiv:2310.18608

  23. Valcarce D, Landin A, Parapar J, Barreiro Á (2019) Collaborative filtering embeddings for memory-based recommender systems. Eng Appl Artif Intell 85:347–356

    Article  Google Scholar 

  24. Zhang J-D, Chow C-Y (2013) igslr: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. SIGSPATIAL’13, pp. 334–343. Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/2525314.2525339

  25. Laß C, Herzog D, Wörndl W (2017) Context-aware tourist trip recommendations. In: Proceedings of the 2nd Workshop on Recommenders in Tourism Co-located with 11th ACM Conference on Recommender Systems (RecSys 2017), Como, Italy

  26. Petrov A, Makarov Y (2021) Attention-based neural re-ranking approach for next city in trip recommendations. arXiv preprint arXiv:2103.12475

  27. Choi SH, Jeong Y-S, Jeong MK (2010) A hybrid recommendation method with reduced data for large-scale application. IEEE Trans Syst Man Cybernet Part C (Appl Rev) 40(5):557–566. https://doi.org/10.1109/TSMCC.2010.2046036

    Article  Google Scholar 

  28. Basaran D, Ntoutsi E, Zimek A (2017) Redundancies in data and their effect on the evaluation of recommendation systems: a case study on the amazon reviews datasets, pp 390–398. https://epubs.siam.org/doi/abs/10.1137/1.9781611974973.44

  29. Chin J Y, Chen Y, Cong G (2022) The datasets dilemma: How much do we really know about recommendation datasets? In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM ’22, pp. 141–149. Association for Computing Machinery, New York, NY, USA . https://doi.org/10.1145/3488560.3498519

  30. Erkal N (2024) Exploring models, data strategies, and hyperparameter tuning in a trip recommendation system. Master’s thesis, Çankaya University

  31. Hewamalage H, Bergmeir C, Bandara K (2021) Recurrent neural networks for time series forecasting: Current status and future directions. Int J Forecast 37(1):388–427

    Article  Google Scholar 

  32. Yang S, Yu X, Zhou Y (2020) Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. In: 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), pp. 98–101 . IEEE

  33. Trinh T, Dai A, Luong T, Le Q (2018) Learning longer-term dependencies in rnns with auxiliary losses. In: International Conference on Machine Learning, pp. 4965–4974 . PMLR

  34. Ghaemmaghami B, Deng Z, Cho B, Orshansky L, Singh A K, Erez M, Orshansky M (2020) Training with Multi-Layer Embeddings for Model Reduction. https://arxiv.org/abs/2006.05623

  35. Chen X, Huang L (2020) Port throughput forecast model based on adam optimized gru neural network. In: Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence, pp. 46–51

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Authors and Affiliations

  1. Computer Engineering Department, Cankaya University, Etimesgut, Ankara, 06790, Türkiye

    Abdullah Uğur Mat & Ayşe Nurdan Saran

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  1. Abdullah Uğur Mat
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  2. Ayşe Nurdan Saran
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Contributions

Abstract and Chapter 1 were written by Ayşe Nurdan Saran, and the rest of the manuscript was written by Abdullah Uğur MAT. Ayşe Nurdan SARAN also serves as supervisor.

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Correspondence to Abdullah Uğur Mat.

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Mat, A.U., Saran, A.N. Enhancing session-based trip recommendations using matrix factorization: a study on algorithm efficiency and resource utilization. J Supercomput 81, 292 (2025). https://doi.org/10.1007/s11227-024-06726-1

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