Customer care is an essential pillar of the e-commerce shopping experience with companies spending millions of dollars each year, employing automation and human agents, across geographies (like US, Canada, Mexico, Chile), channels (like Chat, Interactive Voice Response (IVR)), and languages (like English, Spanish). SOTA pre-trained models like multilingual-BERT, fine-tuned on annotated data have shown good performance in downstream tasks relevant to Customer Care. However, model performance is largely subject to the availability of sufficient annotated domain-specific data. Cross-domain availability of data remains a bottleneck, thus building an intent classifier that generalizes across domains (defined by channel, geography, and language) with only a few annotations, is of great practical value. In this paper, we propose an embedder-cum-classifier model architecture which extends state-of-the-art domain-specific models to other domains with only a few labeled samples. We adopt a supervised fine-tuning approach with isotropic regularizers to train a domain-specific sentence embedder and a multilingual knowledge distillation strategy to generalize this embedder across multiple domains. The trained embedder, further augmented with a simple linear classifier can be deployed for new domains. Experiments on Canada and Mexico e-commerce Customer Care dataset with few-shot intent detection show an increase in accuracy by 20-23% against the existing state-of-the-art pre-trained models.

Factorization machines (FMs) are a state-of-the-art model class for user response prediction in the computational advertising domain. Rapid growth of internet and mobile device usage has given rise to multiple customer touchpoints. This coupled with factors like high cookie churn rate results in a fragmented view of user activity at the advertiser»s end. Current literature assumes procured user signals as the absolute truth, which is contested by the absence of deterministic identity linkage across a user's multiple avatars. In this work, we characterize the data uncertainty using Robust Optimization (RO) paradigm to design approaches that are immune against perturbations. We propose two novel algorithms: robust factorization machine (RFM) and its field-aware variant (RFFM), under interval uncertainty. These formulations are generic and can find applicability in any classification setting under noise. We provide a distributed and scalable Spark implementation using parallel stochastic gradient descent. In the experiments conducted on three real-world datasets, the robust counterparts outperform the baselines significantly under perturbed settings. Our experimental findings reveal interesting connections between choice of uncertainty set and the noise-proofness of resulting models.

Increased interest in web-based education has spurred the proliferation of online learning environments. However, these platforms suffer from high dropout rates due to lack of sustained motivation among the students taking the course. In an effort to address this problem, we propose an incentive-based, instructor-driven approach to orchestrate the interactions in online educational forums (OEFs). Our approach takes into account the heterogeneity in skills among the students as well as the limited budget available to the instructor. We first analytically model OEFs in a non-strategic setting using ideas from lumpable continuous time Markov chains and compute expected aggregate transient net-rewards for the instructor and the students. We next consider a strategic setting where we use the rewards computed above to set up a mixed-integer linear program which views an OEF as a single-leader-multiple-followers Stackelberg game and recommends an optimal plan to the instructor for maximizing student participation. Our experimental results reveal several interesting phenomena including a striking non-monotonicity in the level of participation of students vis-a-vis the instructor's arrival rate.