Information Retrieval System Internship

About Internship

Assistant Professor, Department of Computer Science Engineering, from Sreenidhi University , is accepting interns interested in the field of Computer Science Engineering, in the research area of Artificial intelligence and Machine learning.

This internship focuses on addressing key challenges in recommender systems using advanced techniques in machine learning and data science. The project will explore and implement solutions to the following core problems: 1. Cold Start Problem – Developing strategies to recommend items to new users or introduce new items with minimal or no historical data. 2. Data Sparsity – Tackling the issue of limited user-item interactions, which makes pattern recognition difficult. 3. Popularity Bias – Mitigating the tendency of models to over-recommend popular items while overlooking niche or emerging content. 4. Session-based Recommendations – Designing systems that adapt to users' recent behavior instead of relying solely on long-term history. 5. Explainable Recommendations – Enhancing transparency by generating user-friendly explanations for each recommendation. 6. Hybrid Recommendation Models – Building and evaluating a simple weighted hybrid model that combines collaborative filtering and content-based filtering. The model will be tested on real-world datasets such as those from Flipkart or Amazon to improve accuracy and relevance.