Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.

Predicting churn and designing intervention strategies are crucial for online platforms to maintain user engagement. We hypothesize that predicting churn, i.e. users leaving from the system without further return, is often a delayed act, and it might get too late for the system to intervene. We propose detecting early signs of users losing interest, allowing time for intervention, and introduce a new formulation ofuser fatigue as short-term dissatisfaction, providing early signals to predict long-term churn. We identify behavioral signals predicting fatigue and develop models for fatigue prediction. Furthermore, we leverage the predicted fatigue estimates to develop fatigue-aware ad-load balancing intervention strategy that reduces churn, improving short- and long-term user retention. Results from deployed recommendation system and multiple live A/B tests across over 80 million users generating over 200 million sessions highlight gains for user engagement and platform strategic metrics.

We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85% which is 12% higher than the previous best known system.