Abstract
Recommendation systems are algorithms for suggesting relevant items to users. Generally, the recommendations are expressed in what will be recommended and a value representing the recommendation’s relevance. However, forecasting if the user will buy the recommended item in the next day, week, or month is crucial for companies. The present study describes a process to suggest items from sequential patterns under temporal restrictions. The novelty in our proposal is that the recommendation considers the time when the item will be acquired by proposing a notation grouping items occurring in the same time window. Therefore, our algorithm could predict the itemsets in the next time windows. The document formalizes the recommendation process composed of a time constraint frequent sequential patterns and a structure for predicting the next itemset based on time constrained frequent patterns. Therefore, to validate our proposal, the prediction results are measured in terms of precision and Jaccard index for three different real datasets about credit and debit card transactions, online shopping, and mobile phone App. Our findings demonstrate the pertinence and relevance of our approach.
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Accenture: Banking on value (2019). https://www.accenture.com/nl-en/insights/strategy/banking-on-value. Accessed Jan 2022
Afsar MM, Crump T, Far B (2021) Reinforcement learning based recommender systems: a survey. arXiv preprint arXiv:2101.06286
Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the eleventh international conference on data engineering. IEEE, pp 3–14
Alatrista-Salas H, Guevara-Cogorno A, Maehara Y, Nunez-del Prado M (2020) Efficiently mining gapped and window constraint frequent sequential patterns. In: Torra V, Narukawa Y, Nin J, Agell N (eds) Modeling decisions for artificial intelligence. Springer International Publishing, Cham, pp 240–251
Aleksandrova M, Brun A, Boyer A, Chertov O (2014) What about interpreting features in matrix factorization-based recommender systems as users? In: HT (Doctoral consortium/late-breaking results/workshops). Citeseer
Du N, Dai H, Trivedi R, Upadhyay U, Gomez-Rodriguez M, Song L (2016) Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1555–1564
Fang H, Zhang D, Shu Y, Guo G (2019) Deep learning for sequential recommendation: algorithms, influential factors, and evaluations. arXiv: Information Retrieval
Fumarola F, Lanotte PF, Ceci M, Malerba D (2016) Clofast: closed sequential pattern mining using sparse and vertical id-lists. Knowl Inf Syst 48(2):429–463
Garofalakis MN, Rastogi R, Shim K (1999) Spirit: sequential pattern mining with regular expression constraints. In: VLDB, vol 99, pp 7–10
Gueniche T, Fournier-Viger P, Raman R, Tseng VS (2015) Cpt+: decreasing the time/space complexity of the compact prediction tree. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 625–636 (2015)
Gueniche T, Fournier-Viger P, Tseng VS (2013) Compact prediction tree: a lossless model for accurate sequence prediction. In: International conference on advanced data mining and applications. Springer, pp 177–188 (2013)
Guevara-Cogorno A, Flamand C, Alatrista-Salas H (2015) Copper - constraint optimized prefixspan for epidemiological research. Procedia Comput Sci 63, 433 – 438 (2015). https://doi.org/10.1016/j.procs.2015.08.364. The 6th international conference on emerging ubiquitous systems and pervasive networks (EUSPN 2015)/the 5th international conference on current and future trends of information and communication technologies in healthcare (ICTH-2015)
Hadjieleftheriou M, Srivastava D (2011) Approximate string processing. Now Publishers Inc, Delft
Knizhnik K (2008) Patricia tries: a better index for prefix searches. Dr. Dobb’s J. https://www.drdobbs.com/architecture-and-design/patricia-tries/208800854#
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Lan GC, Hong TP, Lee HY (2014) An efficient approach for finding weighted sequential patterns from sequence databases. Appl Intell 41(2):439–452
Lin MY, Lee SY (2004) Interactive sequence discovery by incremental mining. Inf Sci 165(3–4):187–205
Masseglia F, Poncelet P, Teisseire M (2003) Incremental mining of sequential patterns in large databases. Data Knowl Eng 46(1):97–121
Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsu MC (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(11):1424–1440
Quadrana M, Cremonesi P, Jannach D (2018) Sequence-aware recommender systems. ACM Comput Surv (CSUR) 51(4):1–36
Singhal A, Sinha P, Pant R (2017) Use of deep learning in modern recommendation system: a summary of recent works. Int J Comput Appl 180(7):17–22. https://doi.org/10.5120/ijca2017916055
Srikant R, Agrawal R (1996) Mining sequential patterns: Generalizations and performance improvements. In: International conference on extending database technology. Springer, pp 1–17
Su X (2009) Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 1:1–20. https://doi.org/10.1155/2009/421425
Suneetha K, Rani MU (2012) Web page recommendation approach using weighted sequential patterns and Markov model. Glob J Comput Sci Technol 12(9):1–7
Takács G, Pilászy I, Németh B, Tikk D (2008) Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the 2008 ACM conference on Recommender systems, pp 267–274
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 565–573 (2018)
Wang S, Hu L, Wang Y, Cao L, Sheng QZ, Orgun M (2019) Sequential recommender systems: challenges, progress and prospects. arXiv preprint arXiv:2001.04830
Yan X, Han J, Afshar R (2003) Clospan: Mining: Closed sequential patterns in large datasets. In: Proceedings of the 2003 SIAM international conference on data mining. SIAM, pp 166–177
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
Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Sequential recommender system based on hierarchical attention network. In: IJCAI international joint conference on artificial intelligence
Yun U, Leggett JJ (2006) Wspan: Weighted sequential pattern mining in large sequence databases. In: 2006 3rd international IEEE conference intelligent systems. IEEE, pp 512–517
Zhou M, Ding Z, Tang J, Yin D (2018) Micro behaviors: A new perspective in e-commerce recommender systems. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 727–735
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Galarreta, AP., Samamé, H., Maehara, Y. et al. Recommender systems using temporal restricted sequential patterns. J Ambient Intell Human Comput 14, 15895–15908 (2023). https://doi.org/10.1007/s12652-022-03808-x
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DOI: https://doi.org/10.1007/s12652-022-03808-x