Preprint / Version 1

KAN in Marketing: Application of Kolmogorov-Arnold Network for Purchase Prediction in E-Commerce

Theoretical comparison with conventional deep learning models

##article.authors##

DOI:

https://doi.org/10.51094/jxiv.893

Keywords:

Machine Learning, Deep Learning, Kolmogorov-Arnold Network, Marketing, Purchase Prediction

Abstract

In recent years, machine learning methods, including deep learning, have continued to evolve rapidly. As examples, generative models capable of generating media data and large-scale language models capable of natural communication have emerged, and are having a significant impact in a wide range of fields, not only in the creation of simple applications, but also in transforming the way society works and decision-making processes as a whole. In addition, the Kolmogorov-Arnold Network (KAN) has attracted attention as a model with an architecture different from that of conventional deep learning, and models based on it have been proposed one after another. However, its application in the social sciences has not progressed, and its applicability remains unclear. In this study, we first provide a comprehensive overview of the development of deep learning up to this point, then predict future purchases using KAN, and verify the effect of each parameter through model exploration. In addition, through visualization of the activation function, which is one of the usefulness of KAN, we will examine the possibility of applying KAN to marketing analysis.

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References

Akashi, Shigeo (2001) “Application of ε-entropy theory to Kolmogorov—Arnold representation theorem,” Reports on Mathematical Physics, Vol. 48, No. 1-2, pp. 19–26, DOI: http://dx.doi.org/10.1016/ S0034-4877(01)80060-4.

Ala’raj, Maher, Maysam F Abbod, and Munir Majdalawieh (2021) “Modelling customers credit card behaviour using bidirectional LSTM neural networks,” Journal of Big Data, Vol. 8, No. 1, pp. 1–27, DOI: http://dx.doi.org/10.1186/s40537-021-00461-7.

Altarabichi, Mohammed Ghaith (2024) “DropKAN: Regularizing KANs by masking post-activations,” arXiv preprint arXiv:2407.13044, DOI: http://dx.doi.org/10.48550/arXiv.2407.13044.

Arnol’d, Vladimir I. (1957) “On functions of three variables,” Proceedings of the USSR Academy of Sciences, Vol. 114, pp. 679–681 (English translation: Amer. Math. Soc. Transl., 28 (1963), pp. 51– 54.), DOI: http://dx.doi.org/10.1007/978-3-642-01742-1_2.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio (2015) “Neural machine translation by jointly learning to align and translate,” 3rd International Conference on Learning Representations (ICLR 2015), DOI: http://dx.doi.org/10.48550/arXiv.1409.0473.

Bengio, Yoshua, Pascal Lamblin, Dan Popovici, and Hugo Larochelle (2006) “Greedy layer-wise training of deep networks,” Proceedings of the 19th International Conference on Neural Information Processing Systems (NIPS2006), Vol. 19.

Bengio, Yoshua, Ian Goodfellow, and Aaron Courville (2017) Deep learning, Vol. 1: MIT press Cambridge, MA, USA, DOI: http://dx.doi.org/10.1007/s10710-017-9314-z.

Bodner, Alexander Dylan, Antonio Santiago Tepsich, Jack Natan Spolski, and Santiago Pourteau (2024) “Convolutional Kolmogorov-Arnold Networks,” arXiv preprint arXiv:2406.13155, DOI: http://dx. doi.org/10.48550/arXiv.2406.13155.

Bottou, L ́eon (2012) “Stochastic gradient descent tricks,” in Neural networks: Tricks of the trade: Springer, pp. 421–436.

Bozorgasl, Zavareh and Hao Chen (2024) “Wav-kan: Wavelet kolmogorov-arnold networks,” arXiv preprint arXiv:2405.12832, DOI: http://dx.doi.org/10.48550/arXiv.2405.12832.

Bresson, Roman, Giannis Nikolentzos, George Panagopoulos, Michail Chatzianastasis, Jun Pang, and Michalis Vazirgiannis (2024) “Kagnns: Kolmogorov-arnold networks meet graph learning,” arXiv preprint arXiv:2406.18380, DOI: http://dx.doi.org/10.48550/arXiv.2406.18380.

Brown,TomB(2020)“Languagemodelsarefew-shotlearners,” arXivpreprintArXiv:2005.14165,DOI: http://dx.doi.org/10.48550/arXiv.2005.14165.

Chen, Pei Pei, Anna Guitart, Ana Fern ́andez del R ́ıo, and Africa Peri ́anez (2018) “Customer lifetime value in video games using deep learning and parametric models,” in 2018 IEEE international conference on big data (big data), pp. 2134–2140, IEEE, DOI: http://dx.doi.org/10.1109/BigData.2018. 8622151.

Chen, Tianqi and Carlos Guestrin (2016) “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794, DOI: http://dx.doi.org/10.1145/2939672.2939785.

Cheon, Minjong (2024) “Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing,” arXiv preprint arXiv:2406.00600, DOI: http://dx.doi.org/10.48550/arXiv.2406.00600. Cho, Kyunghyun, Bart Van Merri ̈enboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger

Schwenk, and Yoshua Bengio (2014) “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, DOI: http://dx.doi.org/10. 3115/v1/D14-1179.

Choi, Woo Yong, Kyu Ye Song, and Chan Woo Lee (2018) “Convolutional attention networks for multimodal emotion recognition from speech and text data,” in Proceedings of grand challenge and workshop on human multimodal language (Challenge-HML), pp. 28–34, DOI: http://dx.doi.org/10.18653/ v1/W18-3304.

Cortes,CorinnaandVladimirVapnik(1995)“Support-vectornetworks,” Machinelearning,Vol.20,pp. 273–297, DOI: http://dx.doi.org/10.1007/BF00994018.

Cover, Thomas and Peter Hart (1967) “Nearest neighbor pattern classification,” IEEE transactions on information theory, Vol. 13, No. 1, pp. 21–27, DOI: http://dx.doi.org/10.1109/TIT.1967.1053964.

De Boor, Carl (2003) A practical guide to splines: springer New York, revised edition.

De Caigny, Arno, Kristof Coussement, Koen W De Bock, and Stefan Lessmann (2020) “Incorporating textual information in customer churn prediction models based on a convolutional neural network,” International Journal of Forecasting, Vol. 36, No. 4, pp. 1563–1578, DOI: http://dx.doi.org/10.1016/j.ijforecast.2019.03.029.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova (2019) “Bert: Pre-training of deep bidirectional transformers for language understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long and Short Papers), pp. 4171–4186, DOI: http://dx. doi.org/10.18653/v1/N19-1423.

Drokin, Ivan (2024) “Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies,” arXiv preprint arXiv:2407.01092, DOI: http://dx.doi.org/10.48550/arXiv.2407.01092.

Elfwing, Stefan, Eiji Uchibe, and Kenji Doya (2018) “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural networks, Vol. 107, pp. 3–11, DOI: http: //dx.doi.org/10.1016/j.neunet.2017.12.012.

Elman, Jeffrey L (1990) “Finding structure in time,” Cognitive science, Vol. 14, No. 2, pp. 179–211, DOI: http://dx.doi.org/10.1207/s15516709cog1402_1.

Genet, Remi and Hugo Inzirillo (2024) “Tkan: Temporal kolmogorov-arnold networks,” arXiv preprint arXiv:2405.07344, DOI: http://dx.doi.org/10.48550/arXiv.2405.07344.

Gilardi, Fabrizio, Meysam Alizadeh, and Ma ̈el Kubli (2023) “ChatGPT outperforms crowd workers for text-annotation tasks,” Proceedings of the National Academy of Sciences, Vol. 120, No. 30, e2305016120, DOI: http://dx.doi.org/10.1073/pnas.2305016120.

Graves, Alex and Ju ̈rgen Schmidhuber (2005) “Framewise phoneme classification with bidirectional

LSTM and other neural network architectures,” Neural networks, Vol. 18, No. 5-6, pp. 602–610, DOI: http://dx.doi.org/10.1016/j.neunet.2005.06.042.

Guo, Long, Lifeng Hua, Rongfei Jia, Binqiang Zhao, Xiaobo Wang, and Bin Cui (2019) “Buying or

browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1984–1992, DOI: http://dx.doi.org/10.1145/3292500.3330670.

He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (2016) “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, DOI: http://dx.doi.org/10.1109/CVPR.2016.90.

Hinton, Geoffrey E and Ruslan R Salakhutdinov (2006) “Reducing the dimensionality of data with neural networks,” science, Vol. 313, No. 5786, pp. 504–507, DOI: http://dx.doi.org/10.1126/science. 1127647.

Hinton, Geoffrey E, Simon Osindero, and Yee-Whye Teh (2006) “A fast learning algorithm for deep belief nets,” Neural computation, Vol. 18, No. 7, pp. 1527–1554, DOI: http://dx.doi.org/10.1162/neco. 2006.18.7.1527.

Ho,Jonathan,AjayJain,andPieterAbbeel(2020)“Denoisingdiffusionprobabilisticmodels,” Advances in neural information processing systems, Vol. 33, pp. 6840–6851.

Hochreiter,SeppandJu ̈rgenSchmidhuber(1997)“Longshort-termmemory,” Neuralcomputation,Vol. 9, No. 8, pp. 1735–1780, DOI: http://dx.doi.org/10.1162/neco.1997.9.8.1735.

Hutto, Clayton and Eric Gilbert (2014) “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the international AAAI conference on web and social media, Vol. 8, pp. 216–225, DOI: http://dx.doi.org/10.1609/icwsm.v8i1.14550.

Jacoby, Jacob and Robert W Chestnut (1978) Brand loyalty: Measurement and management: John Wiley & Sons Incorporated.

Joachims, Thorsten (1998) “Text categorization with support vector machines: Learning with many relevant features,” in European conference on machine learning, pp. 137–142, Springer, DOI: http: //dx.doi.org/10.1007/BFb0026683.

Jordan, Michael I (1997) “Serial order: A parallel distributed processing approach,” in Advances in psychology, Vol. 121: Elsevier, pp. 471–495, DOI: http://dx.doi.org/10.1016/S0166-4115(97) 80111-2.

Kamakura, Wagner A and Michel Wedel (1997) “Statistical data fusion for cross-tabulation,” Journal of Marketing Research, Vol. 34, No. 4, pp. 485–498, DOI: http://dx.doi.org/10.1177/ 002224379703400406.

Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu (2017) “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems (NIPS2017), Vol. 30.

Khattar, Anuradha and SMK Quadri (2022) “Camm: Cross-attention multimodal classification of disaster-relatedtweets,” IEEEAccess,Vol.10,pp.92889–92902,DOI:http://dx.doi.org/10.1109/ACCESS.2022.3202976.

Kiamari, Mehrdad, Mohammad Kiamari, and Bhaskar Krishnamachari (2024) “GKAN: Graph Kolmogorov-Arnold Networks,” arXiv preprint arXiv:2406.06470, DOI: http://dx.doi.org/10.48550/arXiv.2406.06470.

Kim, Minsu, Woosik Shin, SeongBeom Kim, and Hee-Woong Kim (2023) “Predicting Session Conversion on E-commerce: A Deep Learning-based Multimodal Fusion Approach,” Asia pacific journal of information systems, Vol. 33, No. 3, pp. 737–767, DOI: http://dx.doi.org/10.14329/apjis.2023. 33.3.737.

Kingma, Diederik P and Jimmy Ba (2014) “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980.

Koehn, Dennis, Stefan Lessmann, and Markus Schaal (2020) “Predicting online shopping behaviour from clickstream data using deep learning,” Expert Systems with Applications, Vol. 150, p. 113342, DOI: http://dx.doi.org/10.1016/j.eswa.2020.113342.

Kolmogorov, A.N. (1956) “On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables,” Proceedings of the USSR Academy of Sciences, Vol. 108, pp. 179–182 (English translation: Amer. Math. Soc. Transl., 17 (1961), pp. 369–373.), DOI: http://dx.doi.org/10.1007/978-3-642-01742-1_5.

K ̈oppen, Mario (2002) “On the training of a Kolmogorov Network,” in Artificial Neural Networks— ICANN 2002: International Conference Madrid, Spain, August 28–30, 2002 Proceedings 12, pp. 474– 479, Springer, DOI: http://dx.doi.org/10.1007/3-540-46084-5_77.

Krugmann, Jan Ole and Jochen Hartmann (2024) “Sentiment Analysis in the Age of Generative AI,” Customer Needs and Solutions, Vol. 11, No. 1, p. 3, DOI: http://dx.doi.org/10.1007/s40547-02400143-4.

Kundu, Akash, Aritra Sarkar, and Abhishek Sadhu (2024) “Kanqas: Kolmogorov arnold network for quantum architecture search,” arXiv preprint arXiv:2406.17630, DOI: http://dx.doi.org/10. 48550/arXiv.2406.17630.

Larochelle, Hugo and Yoshua Bengio (2008) “Classification using discriminative restricted Boltzmann machines,” in Proceedings of the 25th international conference on Machine learning, pp. 536–543, DOI: http://dx.doi.org/10.1145/1390156.1390224.

Le, Quoc and Tomas Mikolov (2014) “Distributed representations of sentences and documents,” in International conference on machine learning (ICML2014), pp. 1188–1196.

LeCun, Yann, Yoshua Bengio et al. (1995) “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks, Vol. 3361, No. 10, p. 1995.

Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Ku ̈ttler, Mike Lewis, Wen-tau Yih, Tim Rockt ̈aschel et al. (2020) “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems (NIPS2020), Vol. 33, pp. 9459–9474.

Lin, Ji-Nan and Rolf Unbehauen (1993) “On the realization of a Kolmogorov network,” Neural Computation, Vol. 5, No. 1, pp. 18–20, DOI: http://dx.doi.org/10.1162/neco.1993.5.1.18.

Liu, Yinhan, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov (2019) “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, DOI: http://dx.doi.org/10.48550/arXiv.1907.11692.

Liu, Ziming, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljaˇci ́c, Thomas Y Hou, and Max Tegmark (2024) “Kan: Kolmogorov-arnold networks,” arXiv preprint arXiv:2404.19756, DOI: http://dx.doi.org/10.48550/arXiv.2404.19756.

Lobanov, Valeriy, Nikita Firsov, Evgeny Myasnikov, Roman Khabibullin, and Artem Nikonorov (2024) “HyperKAN:Kolmogorov-ArnoldNetworksmakeHyperspectralImageClassificatorsSmarter,” arXiv preprint arXiv:2407.05278, DOI: http://dx.doi.org/10.48550/arXiv.2407.05278.

Lorentz, GG (1962) “Metric entropy, widths, and superpositions of functions,” The American Mathematical Monthly, Vol. 69, No. 6, pp. 469–485, DOI: http://dx.doi.org/10.1080/00029890.1962. 11989915.

Loshchilov, I (2017) “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101.

Ma, Liye and Baohong Sun (2020) “Machine learning and AI in marketing–Connecting computing power to human insights,” International Journal of Research in Marketing, Vol. 37, No. 3, pp. 481–504, DOI: http://dx.doi.org/10.1016/j.ijresmar.2020.04.005.

Mena, C Gary, Arno De Caigny, Kristof Coussement, Koen W De Bock, and Stefan Lessmann (2019) “ChurnPredictionwithSequentialDataandDeepNeuralNetworks.AComparativeAnalysis,” arXiv preprint arXiv:1909.11114, DOI: http://dx.doi.org/10.48550/arXiv.1909.11114.

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean (2013) “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, DOI: http://dx.doi.org/10.48550/arXiv.1301.3781.

Ngai, Eric WT and Yuanyuan Wu (2022) “Machine learning in marketing: A literature review, conceptual framework, and research agenda,” Journal of Business Research, Vol. 145, pp. 35–48, DOI: http://dx.doi.org/10.1016/j.jbusres.2022.02.049.

Ngiam, Jiquan, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng (2011) “Multimodal deep learning,” in Proceedings of the 28th international conference on machine learning (ICML-11), pp. 689–696.

Niimi, Junichiro (2024a) “Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation,” arXiv preprint arXiv:2407.13069, DOI: http://dx.doi.org/10.48550/arXiv.2407.13069.

Niimi, Junichiro (2024b) “An Efficient Multimodal Learning Framework to Comprehend Consumer Preferences Using BERT and Cross-Attention,” arXiv preprint arXiv:2405.07435, DOI: http://dx.doi.org/10. 48550/arXiv.2405.07435.

Ostrand, Phillip A (1965) “Dimension of metric spaces and Hilbert’s problem 13.”

Pan, Hong and Hanxun Zhou (2020) “Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce,” Electronic Commerce Research, Vol. 20, No. 2, pp. 297–320, DOI: http://dx.doi.org/10.1007/s10660-020-09409-0.

Pan, Ziyang, Zhishan Huang, Xiaowen Lin, Songxia Li, Huanze Zeng, and Daifeng Li (2020) “Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model,” in Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1–10, DOI: http://dx.doi.org/10.1145/3446132.3446169.

Peters, Matthew E., Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and

Luke Zettlemoyer (2018) “Deep Contextualized Word Representations,” Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237, DOI: http://dx.doi.org/10.18653/ v1/N18-1202.

Poggio, Tomaso, Andrzej Banburski, and Qianli Liao (2020) “Theoretical issues in deep networks,” Proceedings of the National Academy of Sciences, Vol. 117, No. 48, pp. 30039–30045, DOI: http: //dx.doi.org/10.1073/pnas.1907369117.

Polar, Andrew and Michael Poluektov (2021) “A deep machine learning algorithm for construction of the Kolmogorov–Arnold representation,” Engineering Applications of Artificial Intelligence, Vol. 99, p. 104137, DOI: http://dx.doi.org/10.1016/j.engappai.2020.104137.

Prechelt, Lutz (1998) “Early stopping-but when?” in Neural Networks: Tricks of the trade: Springer, pp. 55–69, DOI: http://dx.doi.org/10.1007/978-3-642-35289-8_5.

Ramachandram, Dhanesh and Graham W Taylor (2017) “Deep multimodal learning: A survey on recent advances and trends,” IEEE signal processing magazine, Vol. 34, No. 6, pp. 96–108, DOI: http: //dx.doi.org/10.1109/MSP.2017.2738401.

REES46 Marketing Platform (2020) eCommerce purchase history from electronics store : (M. Kechinov, https://www.kaggle.com/datasets/mkechinov/ecommerce-purchase-history-fromelectronics-store, accessed Apr. 20th, 2024).

Rumelhart, David E, Geoffrey E Hinton, and Ronald J Williams (1986a) Learning internal representations by error propagation, In parallel distributed processing, explorations in the microstructure of cognition, Vol. 1: Foundations, pp. 318-362: MIT Press.

Rumelhart, David E, Geoffrey E Hinton, and Ronald J Williams (1986b) “Learning representations by back-propagating errors,” nature, Vol. 323, No. 6088, pp. 533–536.

Salakhutdinov, Ruslan and Geoffrey Hinton (2009) “Deep boltzmann machines,” in 12th International Conference on Artificial intelligence and statistics (AISTATS) 2009, pp. 448–455, PMLR.

Sanh, Victor, Lysandre Debut, Julien Chaumond, and Thomas Wolf (2019) “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv, DOI: http://dx.doi.org/10.48550/ arxiv.1910.01108.

Schmidt-Hieber, Johannes (2021) “The Kolmogorov–Arnold representation theorem revisited,” Neural networks, Vol. 137, pp. 119–126, DOI: http://dx.doi.org/10.1016/j.neunet.2021.01.020.

Sidharth, SS (2024) “Chebyshev polynomial-based kolmogorov-arnold networks: An efficient architecture for nonlinear function approximation,” arXiv preprint arXiv:2405.07200, DOI: http://dx.doi.org/ 10.48550/arXiv.2405.07200.

Simonyan, Karen and Andrew Zisserman (2014) “Very deep convolutional networks for largescale image recognition,” DOI: http://dx.doi.org/10.48550/arXiv.1409.1556, arXiv preprint arXiv:1409.1556.

Smola, Alex J and Bernhard Sch ̈olkopf (2004) “A tutorial on support vector regression,” Statistics and computing, Vol. 14, pp. 199–222, DOI: http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88. Smolensky, Paul et al. (1986) “Information processing in dynamical systems: Foundations of harmony theory.”

Sparck Jones, Karen (1972) “A statistical interpretation of term specificity and its application in retrieval,” Journal of documentation, Vol. 28, No. 1, pp. 11–21, DOI: http://dx.doi.org/10.1108/eb026526.

Sprecher, David A (1965) “On the structure of continuous functions of several variables,” Transactions of the American Mathematical Society, Vol. 115, pp. 340–355, DOI: http://dx.doi.org/10.2307/1994273.

Srivastava, Nitish and Russ R Salakhutdinov (2012) “Multimodal learning with deep boltzmann machines,” Advances in neural information processing systems, Vol. 25.

Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich (2015) “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, DOI: http: //dx.doi.org/10.1109/CVPR.2015.7298594.

Toth, Arthur, Louis Tan, Giuseppe Di Fabbrizio, and Ankur Datta (2017) “Predicting shopping behavior with mixture of RNNs,” in eCOM@ SIGIR.

Vaca-Rubio, Cristian J, Luis Blanco, Roberto Pereira, and M`arius Caus (2024) “Kolmogorov-arnold networks (kans) for time series analysis,” arXiv preprint arXiv:2405.08790, DOI: http://dx.doi. org/10.48550/arXiv.2405.08790.

Van Atteveldt, Wouter, Mariken ACG Van der Velden, and Mark Boukes (2021) “The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms,” Communication Methods and Measures, Vol. 15, No. 2, pp. 121–140, DOI: http://dx.doi.org/10.1080/19312458.2020.1869198.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin (2017) “Attention is all you need,” Advances in neural information processing systems, Vol. 30, pp. 5998–6008.

Volkmar, Gioia, Peter M Fischer, and Sven Reinecke (2022) “Artificial Intelligence and Machine Learning: Exploringdrivers,barriers,andfuturedevelopmentsinmarketingmanagement,” JournalofBusiness Research, Vol. 149, pp. 599–614, DOI: http://dx.doi.org/10.1016/j.jbusres.2022.04.007.

Wang, Zengzhi, Qiming Xie, Yi Feng, Zixiang Ding, Zinong Yang, and Rui Xia (2023) “Is ChatGPT a good sentiment analyzer? A preliminary study,” arXiv preprint arXiv:2304.04339, DOI: http: //dx.doi.org/10.48550/arXiv.2304.04339.

Xu, Kunpeng, Lifei Chen, and Shengrui Wang (2024) “Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability,” arXiv preprint arXiv:2406.02496, DOI: http://dx. doi.org/10.48550/arXiv.2406.02496.

Yadav, Ashima and Dinesh Kumar Vishwakarma (2020) “Sentiment analysis using deep learning architectures: a review,” Artificial Intelligence Review, Vol. 53, No. 6, pp. 4335–4385, DOI: http: //dx.doi.org/10.1007/s10462-019-09794-5.

Yoon, Seunghyun, Seokhyun Byun, Subhadeep Dey, and Kyomin Jung (2019) “Speech emotion recognition using multi-hop attention mechanism,” in ICASSP 2019-2019 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp. 2822–2826, IEEE, DOI: http://dx.doi.org/ 10.1109/ICASSP.2019.8683483.

Yu, Runpeng, Weihao Yu, and Xinchao Wang (2024) “Kan or mlp: A fairer comparison,” arXiv preprint arXiv:2407.16674, DOI: http://dx.doi.org/10.48550/arXiv.2407.16674.

Zadeh, Amir, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency (2018) “Memory fusion network for multi-view sequential learning,” in Proceedings of the AAAI conference on artificial intelligence, Vol. 32.

Zeng, Chen, Jiahui Wang, Haoran Shen, and Qiao Wang (2024) “KAN versus MLP on Irregular or Noisy Functions,” arXiv preprint arXiv:2408.07906, DOI: http://dx.doi.org/10.48550/arXiv. 2408.07906.

新美潤一郎 (2021) 「時間単位 Clumpiness 指標を用いた解析手法の提案: 生存時間分析と LSTM-RNN を用 いた利用頻度・離脱時間の予測への RFMC 分析の活用」，『名城論叢』，第 22 巻，第 2 号，49–63 頁.

新美潤一郎 (2024) 「異なる次元数のデータを同時に投入した行動的ロイヤルティ推計手法の提案 —Source Attention Transformerと特徴融合によるマルチモーダル深層学習—」，『応用統計学』，第 53 巻，第 1 号， 15–32 頁, DOI: http://dx.doi.org/10.5023/jappstat.53.15.

新美潤一郎・星野崇宏 (2017a) 「Deep Boltzmann Machine を用いたデータ融合手法の提案」，『人工 知能学会全国大会論文集 第 31 回 (2017)』，1I12–1I12 頁，一般社団法人 人工知能学会, DOI: http: //dx.doi.org/10.11517/pjsai.JSAI2017.0_1I12.

新美潤一郎・星野崇宏 (2017b) 「顧客行動の多様性変数を利用した購買行動の予測」，『人工知能学会論文誌』，第 32 巻，第 2 号，B-G63 1–9 頁, DOI: http://dx.doi.org/10.1527/tjsai.B-G63.

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Submitted: 2024-09-16 09:33:22 UTC

Published: 2024-09-19 00:52:13 UTC
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