A5092492046- Recommender Systems and Techniques
- Mobile Crowdsensing and Crowdsourcing
- Advanced Bandit Algorithms Research
- Open Education and E-Learning
- Machine Learning and Algorithms
- Image and Video Quality Assessment
- Explainable Artificial Intelligence (XAI)
- Green IT and Sustainability
- Advanced Graph Neural Networks
- Multimodal Machine Learning Applications
- Algorithms and Data Compression
- Speech and dialogue systems
- Image Retrieval and Classification Techniques
- Topic Modeling
- Software Engineering Research
Karnataka Health Promotion Trust
2023-2024
Ad-load balancing is a critical challenge in online advertising systems, particularly the context of social media platforms, where goal to maximize user engagement and revenue while maintaining satisfactory experience. This requires optimization conflicting objectives, such as satisfaction ads revenue. Traditional approaches ad-load rely on static allocation policies, which fail adapt changing preferences contextual factors. In this paper, we present an approach that leverages off-policy...
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, it might get too late intervene. propose detecting early signs of losing interest, allowing time intervention, introduce new formulation ofuser fatigue as short-term dissatisfaction, providing signals predict long-term churn. identify behavioral develop...
In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task providing code recommendations in multiple formats to traditional RecSys challenges, outline several similarities and differences due domain specifics. We emphasize importance relevant context LLM for use case lessons learned from enhancements & offline online evaluation such AI-assisted systems.
Practitioners who wish to build real-world applications that rely on ranking models, need decide which modelling paradigm follow. This is not an easy choice make, as the research literature this topic has been shifting in recent years. In particular, whilst Gradient Boosted Decision Trees (GBDTs) have reigned supreme for more than a decade, flexibility of neural networks allowed them catch up, and works report accuracy metrics are par. Nevertheless, practical systems require considerations...
Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in Learning-to-Rank (LTR) research literature have enabled rapid growth this application area. Several popular interfaces now include nested lists, where users can enter a 2nd-level feed via any given 1st-level item. Naturally, has implications evaluation metrics, objective functions, and ranking policies we wish learn. We propose...
Fill-in-the-Middle (FIM) models play a vital role in code completion tasks, leveraging both prefix and suffix context to provide more accurate contextually relevant suggestions. This paper presents approaches improve FIM while addressing the challenge of maintaining low latency for real-time coding assistance. We enhance by incorporating curriculum examples training process. identify patterns where suggestions fail frequently, revealing complexities that smaller language struggle with. To...