Madan Gopal Jhawar

Hitesh Sagtani, Madan Jhawar, Rishabh Mehrotra et al.

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.

Madan Gopal Jhawar, Vipindeep Vangala, Nishchay Sharma et al.

Intelligent Personal Assistants (IPAs) have become widely popular in recent times. Most of the commercial IPAs today support a wide range of skills including Alarms, Reminders, Weather Updates, Music, News, Factual Questioning-Answering, etc. The list grows every day, making it difficult to remember the command structures needed to execute various tasks. An IPA must have the ability to communicate information about supported skills and direct users towards the right commands needed to execute them. Users interact with personal assistants in natural language. A query is defined to be a Help Query if it seeks information about a personal assistant's capabilities, or asks for instructions to execute a task. In this paper, we propose an interactive system which identifies help queries and retrieves appropriate responses. Our system comprises of a C-BiLSTM based classifier, which is a fusion of Convolutional Neural Networks (CNN) and Bidirectional LSTM (BiLSTM) architectures, to detect help queries and a semantic Approximate Nearest Neighbours (ANN) module to map the query to an appropriate predefined response. Evaluation of our system on real-world queries from a commercial IPA and a detailed comparison with popular traditional machine learning and deep learning based models reveal that our system outperforms other approaches and returns relevant responses for help queries.

Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee et al.

We present a language independent, unsupervised approach for transforming word embeddings from source language to target language using a transformation matrix. Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language. We initially evaluate our approach via word similarity tasks on a similar language pair - Hindi as source and Urdu as the target language, while we also evaluate our method on French and German as target languages and English as source language. Our approach improves the current state of the art results - by 13% for French and 19% for German. For Urdu, we saw an increment of 16% over our initial baseline score. We further explore the prospects of our approach by applying it on multiple models of the same language and transferring words between the two models, thus solving the problem of missing words in a model. We evaluate this on word similarity and word analogy tasks.