single-jc.php

JACIII Vol.28 No.5 pp. 1210-1222
doi: 10.20965/jaciii.2024.p1210
(2024)

Research Paper:

Dynamic Short-Term Perspective Estimation Based on Formal Concept Analysis

Kazuki Aikawa and Hajime Nobuhara ORCID Icon

Department of Intelligent Interaction Technologies, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Corresponding author

Received:
March 27, 2024
Accepted:
August 2, 2024
Published:
September 20, 2024
Keywords:
perspective estimation, formal concept analysis, hierarchical estimation
Abstract

In online shopping, user perspectives transit dynamically from abstract categories to concrete subcategories within a short period. We propose a perspective-estimation system that estimates the dynamic, short-term perspectives of users by inferring a hierarchy of categories based on their actions. The proposed system analyzes the wish list rankings of users and their operational histories to extract the categories emphasized at that moment. It then employs formal concept analysis to infer the hierarchical structure of categories, thereby visualizing the dynamic short-term perspective. In experiments involving 57 participants, the proposed method rates its match with user perspectives on a seven-point scale, achieving an average score of 4.84, outperforming the feature estimation method using latent Dirichlet allocation (LDA), which scored 4.36. The statistical significance was confirmed through the Wilcoxon rank-sum test with a statistic W=4.80 and a p-value of 1.56×10-6. Compared with LDA, the proposed system is statistically significant in terms of the degree of agreement with the perspectives.

Dynamic short-term perspective estimation based on FCA

Dynamic short-term perspective estimation based on FCA

Cite this article as:
K. Aikawa and H. Nobuhara, “Dynamic Short-Term Perspective Estimation Based on Formal Concept Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.5, pp. 1210-1222, 2024.
Data files:
References
  1. [1] B. Smith and G. Linden, “Two Decades of Recommender Systems at Amazon.com,” IEEE Internet Computing, Vol.21, No.03, pp. 12-18, 2017. https://doi.org/10.1109/MIC.2017.72
  2. [2] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, Vol.7, No.1, pp. 76-80, 2003. https://doi.org/10.1109/MIC.2003.1167344
  3. [3] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer,” Proc. of the 28th ACM Int. Conf. on information and knowledge management, 2019.
  4. [4] W.-C. Kang and J. McAuley, “Self-Attentive Sequential Recommendation,” 2018 IEEE Int. Conf. on Data Mining (ICDM), Singapore, pp. 197-206, 2018. https://doi.org/10.1109/ICDM.2018.00035
  5. [5] M. Balabanović and Y. Shoham, “Fab: Content-based, collaborative recommendation,” Communications of the ACM, Vol.40, No.3, pp. 66-72, 1997.
  6. [6] M. J. Pazzani and D. Billsus, “Content-Based Recommendation Systems,” P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.), “The Adaptive Web,” Lecture Notes in Computer Science, Vol.4321, Springer, Berlin, Heidelberg, 2007. https://doi.org/10.1007/978-3-540-72079-9_10
  7. [7] R. Wille, “Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts,” pp. 445-470, Springer, 1982.
  8. [8] K. Okamoto and R. Fujii, “Introduction to Collaborative Filtering,” Intelligence and Information, Vol.31, No.1, pp. 5-9, 2019. https://doi.org/10.3156/jsoft.31.1_5
  9. [9] F. Ricci, L. Rokach, and B. Shapira, “Introduction to Recommender Systems Handbook,” F. Ricci, L. Rokach, B. Shapira, and P. Kantor (Eds.), “Recommender Systems Handbook,” Springer, Boston, MA, 2011. https://doi.org/10.1007/978-0-387-85820-3_1
  10. [10] B. Smith and G. Linden, “Two Decades of Recommender Systems at Amazon.com,” IEEE Internet Computing, Vol.21, No.3, pp. 12-18, 2017. https://doi.org/10.1109/MIC.2017.72
  11. [11] A. K. Pandey and B. Ankayarkanni, “Recommending E-Commerce Products on Cold Start and Long Tail Using Transaction Data,” 2020 4th Int. Conf. on Trends in Electronics and Informatics (ICOEI), Article No.48184, pp. 661-663, 2020. https://doi.org/10.1109/ICOEI48184.2020.9143009
  12. [12] M. K. K. Devi, R. T. Samy, S. V. Kumar, and P. Venkatesh, “Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems,” 2010 IEEE Int. Conf. on Computational Intelligence and Computing Research, pp. 1-4, 2010. https://doi.org/10.1109/ICCIC.2010.5705777
  13. [13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017.
  14. [14] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint, arXiv:1810.04805, 2018.
  15. [15] Y. Kitamura, I. Sawa, and H. Nobuhara, “A Video Recommendation Method that Explicitly Provides Reasons for Recommendations Using Formal Concept Analysis,” Intelligence and Information, Vol.25, No.1, pp. 624-635, 2013. https://doi.org/10.3156/jsoft.25.624
  16. [16] P. d. Boucher-Ryan and D. Bridge, “Collaborative Recommending using Formal Concept Analysis,” M. Bramer, F. Coenen, and T. Allen (Eds.), “Research and Development in Intelligent Systems XXII,” pp. 205-218, 2006.
  17. [17] S. Kataria and U. Batra, “Co-clustering neighborhood—based collaborative filtering framework using formal concept analysis,” Int. J. of Information Technology, Vol.14, No.4, pp. 1725-1731, 2022.
  18. [18] H. Mezni and T. Abdeljaoued, “A cloud services recommendation system based on Fuzzy Formal Concept Analysis,” Data & Knowledge Engineering, Vol.116, pp. 100-123, 2018. https://doi.org/10.1016/j.datak.2018.05.008
  19. [19] S. Latifi, D. Jannach, and A. Ferraro, “Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics,” Inf. Sci., Vol.609, pp. 660-678, 2022.
  20. [20] G. de S. P. Moreira, S. Rabhi, J. M. Lee, R. Ak, and E. Oldridge, “Transformers4Rec: Bridging the gap between NLP and sequential / session-based recommendation,” Proc. of the 15th ACM Conf. on Recommender Syst., pp. 143-153, 2021.
  21. [21] M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla, V. Bhagwan, and D. Sharp, “E-commerce in your inbox: Product recommendations at scale,” Proc. of the 21st ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 1809-1818, 2015.
  22. [22] S. Wang, L. Cao, Y. Wang, Q. Z. Sheng, M. A. Orgun, and D. Lian, “A survey on session-based recommender systems,” ACM Comput. Surv., Vol.54, No.7, pp. 1-38, 2022.
  23. [23] H. Suzuki and T. Murofushi, “Formal Concept Analysis – Introduction, Support Software, and Applications,” Intelligence and Information, Vol.19, No.2, pp. 103-142, 2007.
  24. [24] A. D. Troy, G.-Q. Zhang, and Y. Tian, “Faster Concept Analysis,” U. Priss, S. Polovina, and R. Hill (Eds.), “Conceptual Structures: Knowledge Architectures for Smart Applications,” LNCS, Vol.4604, 2007.
  25. [25] N. Yoshinaga and H. Nobuhara, “Formal concept analysis based web pages classification/visualization and their application to information retrieval,” 2010 10th Int. Symposium on Communications and Information Technologies, pp. 153-157, 2010. https://doi.org/10.1109/ISCIT.2010.5664895
  26. [26] S. Hirano and K. Aikawa, “Image for trade support application,” Japan Patent, Design Registration No.1708906, 2022.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 04, 2024