Advertisement

Mentor Pattern Identification from Product Usage Logs

  • Ankur Garg
  • Aman Kharb
  • Yash H. Malviya
  • J. P. Sagar
  • Atanu R. Sinha
  • Iftikhar Ahamath Burhanuddin
  • Sunav ChoudharyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

A typical software tool for solving complex problems tends to expose a rich set of features to its users. This creates challenges such as new users facing a steep onboarding experience and current users tending to use only a small fraction of the software’s features. This paper describes and solves an unsupervised mentor pattern identification problem from product usage logs for softening both challenges. The problem is formulated as identifying a set of users (mentors) that satisfies three mentor qualification metrics: (a) the mentor set is small, (b) every user is close to some mentor as per usage pattern, and (c) every feature has been used by some mentor. The proposed solution models the task as a non-convex variant of an Open image in new window regularized logistic regression problem and develops an alternating minimization style algorithm to solve it. Numerical experiments validate the necessity and effectiveness of mentor identification towards improving the performance of a k-NN based product feature recommendation system for a real-world dataset. Further, t-SNE visuals demonstrate that the proposed algorithm achieves a trade-off that is both quantitatively and qualitatively distinct from alternative approaches to mentor identification such as Maximum Marginal Relevance and K-means.

Keywords

Mentor identification Unsupervised learning Sparsity-coverage trade-off L1-regularized logistic regression 

Supplementary material

482301_1_En_28_MOESM1_ESM.pdf (198 kb)
Supplementary material 1 (pdf 197 KB)

References

  1. 1.
    Aggarwal, C.C., Bhuiyan, M.A., Hasan, M.A.: Frequent pattern mining algorithms: a survey. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 19–64. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07821-2_2CrossRefGoogle Scholar
  2. 2.
    Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 532–539. ACM, New York (2009).  https://doi.org/10.1145/1571941.1572033
  3. 3.
    Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise identification using email communications. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 528–531. ACM, New York (2003).  https://doi.org/10.1145/956863.956965
  4. 4.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 335–336. ACM, New York (1998).  https://doi.org/10.1145/290941.291025
  5. 5.
    Fernandes, J.F.N.: Softening the Learning Curve of Software Development Tools. Master’s thesis, Universidade Técnica de Lisboa, Portugal, October 2011Google Scholar
  6. 6.
    Goldsmith, M., Lyons, L., Freas, A.: Coaching for Leadership: How the World’s Greatest Coaches Help Leaders Learn. Jossey Bass/Pfeiffer, Hoboken (2000)Google Scholar
  7. 7.
    Grossman, T., Fitzmaurice, G.: An investigation of metrics for the in situ detection of software expertise. Hum.-Comput. Interact. 30(1), 64–102 (2015)CrossRefGoogle Scholar
  8. 8.
    Gupta, M.R., Chen, Y.: Theory and Use of the EM Algorithm, vol. 4. Now Publishers Inc., Delft (2011)Google Scholar
  9. 9.
    Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  10. 10.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 230–237. ACM, New York (1999).  https://doi.org/10.1145/312624.312682
  11. 11.
    Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The lumiere project: bayesian user modeling for inferring the goals and needs of software users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, UAI 1998, pp. 256–265. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  12. 12.
    Lee, S.I., Lee, H., Abbeel, P., Ng, A.Y.: Efficient l1 regularized logistic regression. In: Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI-06), pp. 1–9 (2006)Google Scholar
  13. 13.
    Ma, D., Schuler, D., Zimmermann, T., Sillito, J.: Expert recommendation with usage expertise. In: 25th IEEE International Conference on Software Maintenance (ICSM 2009), 20–26 September 2009, Edmonton, Alberta, Canada, pp. 535–538 (2009).  https://doi.org/10.1109/ICSM.2009.5306386
  14. 14.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)zbMATHGoogle Scholar
  15. 15.
    Matejka, J., Li, W., Grossman, T., Fitzmaurice, G.: CommunityCommands: command recommendations for software applications. In: Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology, UIST 2009, pp. 193–202. ACM, New York (2009).  https://doi.org/10.1145/1622176.1622214
  16. 16.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. ACM, New York (1994)Google Scholar
  17. 17.
    Wu, T.T., Chen, Y.F., Hastie, T., Sobel, E., Lange, E.: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6), 714–721 (2009).  https://doi.org/10.1093/bioinformatics/btp041CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ankur Garg
    • 1
  • Aman Kharb
    • 2
  • Yash H. Malviya
    • 3
  • J. P. Sagar
    • 4
  • Atanu R. Sinha
    • 5
  • Iftikhar Ahamath Burhanuddin
    • 5
  • Sunav Choudhary
    • 5
    Email author
  1. 1.The University of Texas at AustinAustinUSA
  2. 2.Indian Institute of Technology KharagpurKharagpurIndia
  3. 3.Apple Inc.CupertinoUSA
  4. 4.Indian Institute of Technology MadrasChennaiIndia
  5. 5.Adobe ResearchBangaloreIndia

Personalised recommendations