Enhanced customer experience and engagement by providing personalized travel recommendations, helping users discover ideal destinations tailored to their preferences and travel history.
Boosted user retention and business outcomes by delivering value-added services in the travel domain.
Accomplishments:
Built a graph-based recommendation system that leveraged the connections between destinations (e.g., shared travel themes, geographic proximity, or user-generated preferences) to suggest ideal cities for travelers.
Successfully implemented knowledge graph embeddings and graph neural networks to capture nuanced relationships between destinations and travelers.
Demonstrated measurable improvements in recommendation relevance and user satisfaction, validating the effectiveness of the graph-based approach.
Transferable Skills:
Have understanding of graph-based machine learning techniques, including graph embeddings, collaborative filtering, and hybrid recommendation strategies.
Proficiency with graph analytics tools such as NetworkX, Neo4j, and PyTorch Geometric, as well as scalable distributed systems using Spark GraphX and MLlib.
Strong expertise in personalized recommendation systems, integrating content-based and graph-driven insights into dynamic models.
Hands-on experience in applying machine learning techniques to domain-specific problems, such as travel or e-commerce.
Responsibilities:
Designed and implemented a personalized travel recommendation system by analyzing connections between destinations, user preferences, and travel patterns.
Developed robust data pipelines to preprocess, structure, and analyze travel data for graph-based modeling.
Integrated the recommendation system into a travel platform to deliver tailored suggestions to end-users in real-time.
Challenges and Difficulties:
Addressed challenges such as dataset sparsity due to limited user feedback and travel history, requiring innovative techniques like graph augmentation and transfer learning.
Managed computational complexity and resource constraints when scaling graph algorithms to handle large, interconnected travel data.
Ensured recommendations were contextually relevant across diverse travel scenarios, balancing user preferences and business objectives.
