The rise in popularity of social media platforms, has resulted in millions of new, content pieces being created every day. This surge in content creation underscores the need to pay attention to our design choices as they can greatly impact how long content remains relevant. In today's landscape where regularly recommending new content is crucial, particularly in the absence of detailed information, a variety of factors such as UI features, algorithms and system settings contribute to shaping the journey of content across the platform. While previous research has focused on how new content affects users' experiences, this study takes a different approach by analyzing these decisions considering the content itself. Through a series of carefully crafted experiments we explore how seemingly small decisions can influence the longevity of content, measured by metrics like Content Progression (CVP) and Content Survival (CSR). We also emphasize the importance of recognizing the stages that content goes through underscoring the need to tailor strategies for each stage as a one size fits all approach may not be effective. Additionally we argue for a departure from traditional experimental setups in the study of content lifecycles, to avoid potential misunderstandings while proposing advanced techniques, to achieve greater precision and accuracy in the evaluation process.

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.

With the steady emergence of community question answering (CQA) platforms like Quora, StackExchange, and WikiHow, users now have an unprecedented access to information on various kind of queries and tasks. Moreover, the rapid proliferation and localization of these platforms spanning geographic and linguistic boundaries offer a unique opportunity to study the task requirements and preferences of users in different socio-linguistic groups. In this study, we implement an entity-embedding model trained on a large longitudinal dataset of multi-lingual and task-oriented question-answer pairs to uncover and quantify the (i) prevalence and distribution of various online tasks across linguistic communities, and (ii) emerging and receding trends in task popularity over time in these communities. Our results show that there exists substantial variance in task preference as well as popularity trends across linguistic communities on the platform. Findings from this study will help Q&A platforms better curate and personalize content for non-English users, while also offering valuable insights to businesses looking to target non-English speaking communities online.

Users of music streaming, video streaming, news recommendation, Offline evaluation is challenging because the deployed recommender and e-commerce services often engage with content in a sequential decides which items the user sees, introducing significant manner. Providing and evaluating good sequences of recommendations exposure bias in logged data [7, 16, 22]. Various methods have been is therefore a central problem for these services. Prior proposed to mitigate bias using counterfactual evaluation. In this reweighting-based counterfactual evaluation methods either suffer paper, we use terminology from the multi-armed bandit framework from high variance or make strong independence assumptions to discuss these methods: the recommender performs an action about rewards. We propose a new counterfactual estimator that allows by showing an item depending on the observed context (e.g., user for sequential interactions in the rewards with lower variance covariates, item covariates, time of day, day of the week) and then in an asymptotically unbiased manner. Our method uses graphical observes a reward through the user response (e.g., a stream, a purchase, assumptions about the causal relationships of the slate to reweight or length of consumption) [14]. The recommender follows the rewards in the logging policy in a way that approximates the a policy distribution over actions by drawing items stochastically expected sum of rewards under the target policy. Extensive experiments conditioned on the context. in simulation and on a live recommender system show that The basic idea of counterfactual evaluation is to estimate how a our approach outperforms existing methods in terms of bias and new policy would have performed if it had been deployed instead data efficiency for the sequential track recommendations problem. of the deployed policy.