Abstract:
"This research addresses the intricate domain of culinary, query-to-tag recommendation within the context of food blog platforms, with a specific focus on Extreme Multi-Label Classification (XMLC) techniques. The primary objective is to enhance the accuracy, efficiency, and responsiveness of query-to-tag recommendations, ultimately enriching the user experience.
To achieve this, a framework implemented offering a versatile and modular approach to deep extreme multi-label learning. The research delves into critical challenges, such as data scarcity, scalability, and real-time response demands, offering solutions that encompass feature architecture design, sub-linear search structures, and efficient shortlisting mechanisms. The operational objectives encompass data collection, model development, and real-time inference mechanisms, leading to the integration of the XMLC-based recommendation system into a food blog platform prototype. The platform showcases seamless functionality, connecting users with pertinent culinary tags and recommendations. While the research achieves its goals, it acknowledges limitations, including data availability constraints and potential scalability issues as the platform evolves. Future enhancements are envisioned, encompassing data augmentation, user-generated content, personalization, real-time updates, and internationalization.
In conclusion, this research introduces a framework and application that significantly advance the field of culinary content recommendation. By addressing challenges and charting a course for future improvements, it not only elevates the user experience but also paves the way for innovative culinary exploration in the digital realm."
