Optimizing Recommendations using Fine-Tuned LLMs
This addresses the problem of rigid user queries in digital media platforms, offering a more intuitive way for users to express preferences, though it appears incremental as it builds on existing LLM fine-tuning techniques.
The paper tackles the limitation of traditional keyword-based recommendation systems by generating synthetic datasets that model real-world user interactions with complex preferences like mood and plot details, enabling more expressive natural language queries. This approach enhances personalization and accuracy, establishing a foundation for conversational AI-driven recommendation systems in digital entertainment.
As digital media platforms strive to meet evolving user expectations, delivering highly personalized and intuitive movies and media recommendations has become essential for attracting and retaining audiences. Traditional systems often rely on keyword-based search and recommendation techniques, which limit users to specific keywords and a combination of keywords. This paper proposes an approach that generates synthetic datasets by modeling real-world user interactions, creating complex chat-style data reflective of diverse preferences. This allows users to express more information with complex preferences, such as mood, plot details, and thematic elements, in addition to conventional criteria like genre, title, and actor-based searches. In today's search space, users cannot write queries like ``Looking for a fantasy movie featuring dire wolves, ideally set in a harsh frozen world with themes of loyalty and survival.'' Building on these contributions, we evaluate synthetic datasets for diversity and effectiveness in training and benchmarking models, particularly in areas often absent from traditional datasets. This approach enhances personalization and accuracy by enabling expressive and natural user queries. It establishes a foundation for the next generation of conversational AI-driven search and recommendation systems in digital entertainment.