Recommender Systems Final Year Projects with Source Code

Byadmin Final Year Projects

Recommender Systems Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Recommender Systems projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Recommender Systems Final Year Projects

  1. Cloud-Based K-Closest Pairs Discovery in Dynamic Cyber-Physical-Social Systems
    This project focuses on finding the closest pairs of objects between two sets in complex systems that change over time, like social networks or smart cities. The challenge is that each connection has multiple properties and can change, making the problem very hard to solve. The researchers developed a new cloud-based method that splits the problem into smaller parts to find the closest pairs efficiently. Tests on real datasets show that this method works faster and better than existing approaches.
  2. An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph
    This project improves how online recommendation systems understand users. It uses both the text of user reviews and user ratings to learn what people like. The model studies the full structure of review sentences, not just nearby words. It then combines this with rating patterns to give more accurate recommendations.
  3. Comparison of Real-Time and Batch Job Recommendations
    This project focuses on improving recommendation systems that suggest items like jobs to users. It compares traditional batch-based methods with a real-time approach that considers users’ latest actions. Using a graph-based model, the real-time system gave better results, leading to more user engagement. The study shows how real-time recommendations can be more effective in practical applications.
  4. Optimal Recommendation Models Based on Knowledge Representation Learning and Graph Attention Networks
    This project improves recommendation systems using knowledge graphs. It creates a new model, Cluster TransD, that efficiently represents items and their relationships. Another model, Cluster TransD-GAT, considers how users value different item connections. Experiments show these models give more accurate and relevant recommendations than existing methods.
  5. Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization
    This project develops a smarter recommendation system for online platforms. It analyzes user reviews to understand preferences and feelings. Then it combines this information with ratings to predict what users will like. Tests on Amazon data show it works better than traditional methods.
  6. SoLGR Social Enhancement Group Recommendation via Light Graph Convolution Networks
    This project focuses on improving group recommendations on social networks. It studies how to represent users, items, and groups using their interaction and social connection data. The researchers designed a new model called SoLGR, which uses graph-based methods to combine these representations. The method helps make better predictions for what groups and individual users might like, and it works well on real-world datasets.
  7. Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality
    This project focuses on creating a virtual copy of a real-world object, called a digital twin, which can monitor and manage it using data from sensors. The digital twin is classified into six levels, from basic monitoring to fully autonomous operation, to show its capabilities. The study uses a smart house to demonstrate how these digital twins can be built and visualized in virtual reality. This approach helps users understand and improve the performance of physical assets.
  8. Travel Direction Recommendation Model Based on Photos of User Social Network Profile
    This project creates a smart travel recommendation system using photos from a user’s social media account. It analyzes images and related data to suggest countries the user might like to visit. The system uses machine learning methods to compare, classify, and group data for accurate suggestions. Tests show it can correctly predict trips most of the time, and it can improve further by using photo location information.
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