JACIII Vol.21 No.1 pp. 25-30
doi: 10.20965/jaciii.2017.p0025

Invited Paper:

Web Intelligence and Artificial Intelligence

Yasufumi Takama

Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

October 5, 2016
October 31, 2016
Online released:
January 20, 2017
January 20, 2017
fuzzy inference, multi-agent, support vector machine(s)

This paper briefly summarizes the progress of artificial intelligence (AI) and web intelligence (WI) in the last two decades. The reason why we mention AI and WI together is because those have strong relationship with each other. This paper first summarizes the history of AI, and then gives brief description of supervised learning, which I think has played a major role in AI in the last two decades. As most history of WI is in the target decades, this paper first briefly describes major WI topics, and then gives more detailed description about information recommendation, which I think one of more successful and necessary technologies in practical use.

  1. [1] R. Brooks, “Intelligence without Representation,” Artificial Intelligence, Vol.47, pp. 139-159, 1991.
  2. [2] B. Goertzel and C. Pennachin (Eds.), Artificial General Intelligence, Springer, 2007.
  3. [3] K. Saastamoinen and J. Ketola, “Fuzzy Logic and Differential Evolution Based Expert System for Defining Top Athlete’s Aerobic and Anaerobic Thresholds,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.9, No.5, pp. 534-539, 2005.
  4. [4] E. H. Shortliffe, “Mycin: A Knowledge-Based Computer Program Applied to Infectious Diseases,” Proc. Annual Symposium on Computer Application in Medical Care, pp. 66-69, 1977.
  5. [5] H. Yoshino, “Legal Expert Project,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.1, No.2, pp. 83-85, 1997.
  6. [6] R. Coulom, “Efficient selectivity and backup operators in Monte-Carlo tree search,” CG’06, pp. 72-83, 2006.
  7. [7] D. Silver, et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, Vol.529, pp. 484-489, 2016.
  8. [8] V. N. Vapnik and A. Y. Lerner, “Pattern Recognition Using Generalized Portraits,” Automation and Remote Control, Vol.24, No.6, pp. 774-780, 1963.
  9. [9] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” COLT’92, pp. 144-152, 1992.
  10. [10] Q. V. Le, et al., “Building High-level Features Using Large Scale Unsupervised Learning,” ICML2012, 2012.
  11. [11] K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, “Constrained K-means Clustering with Background Knowledge,” ICML2001, pp. 577-584, 2001.
  12. [12] D. M. Blei, A. Ng, and M. Jordan, “Latent Dirichlet Allocation,” J. of Machine Learning Research, Vol.3, No.5, pp. 993-1022, 2003.
  13. [13] Y. Takama and T. Tonegawa, “Interactive Document Clustering System Based on Coordinated Multiple Views,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.20, No.1, pp. 139-145, 2016.
  14. [14] M. Okabe and S. Yamada, “Active Sampling for Constrained Clustering,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.2, pp. 232-238, 2014.
  15. [15] Y. Kanzawa, Y. Endo, and S. Miyamoto, “Semi-Supervised Fuzzy c-Means Algorithm by Revising Dissimilarity Between Data,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.15, No.1, pp. 95-101, 2011.
  16. [16] H. Lieberman, “Letizia: an Agent That Assits Web Browsing,” IJCAI’95, pp. 924-929, 1995.
  17. [17] Y. S. Maarek, M. Jacovi, M. Shtalhaim, S. Ur, D. Zernik, and I. Z. Ben Shaul, “WebCutter: A system for dynamic and tailorable site mapping,” Proc. 6th Int. World Wide Web Conference, 1997.
  18. [18] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” Stanford InfoLab Technical Report, No.1999-66, 1999.
  19. [19] M. Gori and A. Pucci, “ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines,” IJCAI-07, pp. 2766-2771, 2007.
  20. [20] K. Crammer and Y. Singer, “Pranking with Ranking,” NIPS14, pp. 641-647, 2001.
  21. [21] T. Joachims, “Optimizing Search Engines using Clickthrough Data,” KDD’02, pp. 133-142, 2002.
  22. [22] S. Bajracharya, T. Ngo, E. Linstead, P. Rigor, Y. Dou, P. Baldi, and C. Lopes, “Sourcerer: a search engine for open source code supporting structure-based search,” Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications, pp. 681-682, 2006.
  23. [23] Y. Takama, Y. Zhu, S. Kori, K. Yamaguchi, L. Chen, and H. Ishikawa, “Design of Context Search Engine Based on Analysis of User’s Search Intentions,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.20 No.6, pp. 910-918, 2016.
  24. [24] D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. A. Kalyanpur, A. Lally, J. W. Murdock, E. Nyberg, J. Prager, N. Schlaefer, and C. Welty, “Building Watson: An Overview of the DeepQA Project,” AI Magazine, Vol.31, No.3, pp. 59-79, 2010.
  25. [25] X. Liu and T. Murata, “An Efficient Algorithm for Optimizing Bipartite Modularity in Bipartite Networks,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.14, No.4, pp. 408-415, 2010.
  26. [26] J. M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” ACM-SIAM Symposium on Discrete Algorithms, pp. 668-677, 1998.
  27. [27] C. Silverstein, M. Henzinger, H. Marais, and M. Moricz, “Analysis of a Very Large Web Search Engine Query Log,” SIGIR’99, pp. 6-12, 1999.
  28. [28] C. Lin and Y. He, “Joint Sentiment/Topic Model for Sentiment Analysis,” CIKM’09, pp. 375-384, 2009.
  29. [29] F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Trans. on Interactive Intelligent Systems, Vol.5, Issue 4, Article No.19, 2016.
  30. [30] S. Hattori and Y. Takama, “Recommender System Employing Personal-Vallue-Based User Model,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.2, pp. 157-165, 2014.
  31. [31] Y. Takama, T. Yamaguchi, and S. Hattori, “Personal Value-based Item Modeling and Its Application to Recommendation with Explanation,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.20 No.6, pp. 867-874, 2016.
  32. [32] Y. Takama and Y. Muto, “Profile Generation for TV Program Recommendation Based on Utterance Analysis,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), No.13, No.2, pp. 86-90, 2009.
  33. [33] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” CSCW ’94, pp. 175-186, 1994.
  34. [34] C. Desrosiers and G. Karpis, “A comprehensive survey of neighborhood-based recommendation methods,” Eds. F. Ricci, L. Rokach, B. Shapira, and B. P. Kantor, Recommender Systems Handbook, pp. 107-144, 2011.
  35. [35] D. Jannach, L. Lerche, F. Gedikli, and G. Bonnin, “What recommenders recommend - An analysis of accuracy, popularity, and sales diversity effects,” UMAP2013, pp. 25-37, 2013.
  36. [36] S. Larrain, C. Trattner, D. Parra, E. Graells-Garrido, and K. Nørvåg, “Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging,” RecSys’15, 2015.
  37. [37] P. Adamopoulos and A. Tuzhilin, “On Over-Specialization and Concentration Bias of Recommendations: Probabilistic Neighborhood Selection in Collaborative Filtering Systems,” RecSys’14, pp. 153-160, 2014.
  38. [38] Y. Koren, “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model,” SIGKDD, pp. 426-434, 2008.
  39. [39] R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” NIPS2008, pp. 1257-1264, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017