Mentor Pattern Identification from Product Usage Logs
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 softwares 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 1-norm 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.
Mentor identification Unsupervised learning Sparsity-coverage trade-off L1-regularized logistic regression
Ankur Garg Aman Kharb Yash H.Malviya J.P.Sagar Atanu R.Sinha Iftikhar Ahamath Burhanuddin Sunav Choudhary
The University of Texas at Austin,Austin,USA Indian Institute of Technology Kharagpur,Kharagpur,India Apple Inc.,Cupertino,USA Indian Institute of Technology Madras,Chennai,India Adobe Research,Bangalore,India
国际会议
澳门
英文
359-371
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）