Abstract
We plan to recommend some initial suitable single-itemed sequences like a flight itinerary based on a preference pattern in the form of personalized sequential pattern to each cold-start user. However, sequential pattern mining has never treated a conventional sequential pattern as a personalized pattern. Besides, as a cold-start user lacks the personalized sequential pattern, collaborative filtering cannot recommend one any single-itemed sequences. Thus, we first design such a preference pattern, namely representative sequential pattern, which reflects one’s main frequently recurring buying behavior mined from the item-sequences during a time period. After sampling a training-set from non-cold-start users who prefer similar items, we propose an auxiliary algorithm to mine the representative sequential pattern as the sequential class labels of each training instance. A multi-label classifier seems therefore be trained to predict the sequential-label for each cold-start user based on one’s features. However, most multi-label classification methods are designed to classify data whose class labels are non-sequential. Besides, some of the predictor attributes would be multi-valued in the real world. Aiming to handle such data, we have developed a novel algorithm, named MSDT (Multi-valued and Sequential-labeled Decision Tree). Experimental results indicate it outperforms all the baseline multi-label algorithms in accuracy even if three of them are deep learning algorithms.
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References
Agrawal R, Ghosh S, Imielinski T, Iyer B, Swami A (1992) An interval classifier for database mining applications. In: Proceedings of VLDB’92, Vancouver, pp 560–573
Ayres J, Flannick J, Gehrke J, Yiu T (2002) Sequential pattern mining using a bitmap representation. In: Proceedings of ACM SIGKDD KDD’02, Edmonton, pp 429–435
Biswas S, Lakshmanan LVS, Ray SB (2017) Combating the Cold Start User Problem in Model Based Collaborative Filtering. arXiv:1703.00397 [cs.IR]
Breiman L (2001) Random Forests. Mach Learn 45(1):5–32
Chen YL, Hsu CL, Chou SC (2003) Constructing a multi-valued and multi-labeled decision tree. Expert Syst Appl 25(2):199–209
Chen WJ, Shao YH, Li CN, Deng NY (2016) MLTSVM: A novel twin support vector machine to multi-label learning. Pattern Recognit 52:61–74
Chou S C, Hsu CL (2005) MMDT: A multi-valued and multi-labeled decision tree classifier for data mining. Expert Syst Appl 28(4):799–812
Dhaliwal J, Puglisi SJ, Turpin A (2012) Practical efficient string mining. IEEE Trans Knowl Data Eng 24(4):735–744
Fader PS, Hardie BGS (2001) Forecasting repeat sales at CDNOW: a case study. Interfaces 31(3):S94–S107
Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of IJCAI’93, Chambery, vol 2, pp 1022–1027
Fournier-Viger P, Wu CW, Tseng VS (2013) Mining maximal sequential patterns without candidate maintenance. In: Proceedings of ADMA’13, Hangzhou, pp 169–180
Fournier-Viger P, Wu CW, Gomariz A, Tseng VS (2014A) VMSP: Efficient vertical mining of maximal sequential patterns. In: Proceedings of Canadian AI’14, Montréal, pp 83–94
Fournier-Viger P, Gomariz A, Gueniche T, Soltani A, Wu CW, Tseng VS (2014b) SPMF: A java open-source pattern mining library. J Mach Learn Res 15:3569–3573
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42
Graham B, Dodd D (2008) Security analysis, 6th edn. McGraw-Hill, New York
Han J, Kamber M (2006) Data mining: Concepts and techniques, 2nd edn. Morgan Kaufmann, San Francisco
Jaro MA (1989) Advances in record-linkage methodology as applied to matching the 1985 census of tampa, florida. J Am Stat Assoc 84(406):414–420
Kadous MW, Sammut C (2005) Classification of multivariate time series and structured data using constructive induction. Mach Learn 58:179–216
Kohavi R (1995) A study of cross validation and bootstrap for accuracy estimation and model selection. In: Proceedings of IJCAI’95, Montreal, pp 1137–1143
Osojnik A, Panov P (2018) Tree-based methods for online multi-target regression. Intell Inf Syst 50(2):315–339
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. Mach Learn Res 12:2825–2830
Plantevit M, Choong YW, Laurent A, Laurent D, Teisseire M (2005) M2SP: Mining sequential patterns among several dimensions. In: Proceedings of PKDD’05, Porto, pp 205–216
Quinlan JR (1979) Discovering rules from large collections of examples: a case study. In: Michie D (ed) Expert systems in the microelectronic age. 6th edn. Edinburgh University Press, Edinburgh, pp 169–201
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Quinlan JR (1993) C4.5: Programs for machine learn. Morgan Kaufmann, San Mateo
Raiko T, Valpola H, Lecun Y (2012) Deep learning made easier by linear transformations in perceptrons. In: Proceedings of PMLR, La Palma, vol 22, pp 924–932
Read J, Martino L, Luengo D, Olmos P (2015) Scalable multi-output label prediction: From classifier chains to classifier trellises. Pattern Recogn 48(6):2096–2109
Sahoo N, Singh PV, Mukhopadhyay T (2012) A hidden Markov model for collaborative filtering. MIS Q 36(4):1329–1356
Schlüter T, Conrad S (2012) Hidden markov model-based time series prediction using motifs for detecting inter-time-serial correlations. In: Proceedings of ACM SAC’12, Riva del Garda, pp 158–164
Scikit-learn developers (2018) Scikit-learn user guide Release Release 0.19.2. https://scikit-learn.org/0.19/_downloads/scikit-learn-docs.pdf
Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Lecture notes in computer science. Springer, Berlin, p 4321
Steinberg D, Colla P (2009) Cart: Classification and regression trees. In: Wu X, Kumar V (eds) The top ten algorithms in data mining, vol 9. CRC press, pp 179–203
Szymański P, Kajdanowicz T (2019) Scikit-multilearn: a scikit-based Python environment for performing multi-label classification. J Mach Learn Res 20(1):209–230
Szymański P, Kajdanowicz T, Kersting K (2016) How is a data-driven approach better than random choice in label space division for multi-label classification? Entropy 18(282):1–30
Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Addison Wesley, Boston
Tsai CJ (2014) A study of improving the performance of mining multi-valued and multi-labeled data. Inf-Lithuan 25:95–111
Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-Labelsets for Multilabel Classification. IEEE Knowl Data En 23(7):1079–1089
Wang M, Iyer B, Vitter JS (1998) Scalable mining for classification rules in relational databases. In: Proceedings of IDEAS’98, Cardiff, pp 58–67
Winkler WE (2006) Overview of record linkage and current research directions, research report series, statistics #2006-2. U.S Census Bureau, Washington
Xiao S, Dong M (2015) Hidden semi-Markov model-based reputation management system for online to offline (O2O) e-commerce markets. Decis Support Syst 77:87–99
Xing Z, Pei J, Keogh E (2010) A brief survey on sequence classification. ACM SIGKDD Explor Newsl 12(1):40–48
Zaki M J (2001) Spade: an efficient algorithm for mining frequent sequences. Mach Learn 42(1-2):31–60
Zhang ML, Zhou ZH (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048
Zheng Y (2015) Trajectory data mining: an overview. ACM Trans on Intell Syst and Technol 6(3:29):1–41
Zhu X, Davidson I (2007) Knowledge discovery and data mining: Challenges and realities. IGI Global, Hershey
Acknowledgements
I am very grateful to Professor Ray-I Chang and Huichen Huang for refining the writing of this paper; and those authors for sharing their API or open source codes: Jonathan Liang and Oliver Mannion, the authors of the API, CasperDataSets; Philippe Fournier-Viger, the author of the SPAM algorithm; Philippe Fournier-Viger and Antonio Gomariz, the authors of the VMSP algorithm; and all the authors of APIs in Python or Java, used to code all the baseline algorithms.
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Hsu, CL. A multi-valued and sequential-labeled decision tree method for recommending sequential patterns in cold-start situations. Appl Intell 51, 506–526 (2021). https://doi.org/10.1007/s10489-020-01806-0
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DOI: https://doi.org/10.1007/s10489-020-01806-0