LLM Power Product Search

The Gemini-Powered PSA Inspiration In the age of online shopping, information overload is a real struggle. Sifting through endless product listings, comparing prices, and deciphering reviews can be a daunting task. I envisioned a personal shopping assistant that could bridge this gap, leveraging the power of AI to make online shopping a breeze. Learning Journey This project embarked on a deep dive into the world of natural language processing (NLP) and machine learning. Understanding how to leverage a large language model like Gemini for product search and sentiment analysis was a key learning curve. Integrating with various e-commerce platforms and parsing product data presented another set of challenges. Building the PSA The core of the PSA is built upon Gemini's NLP capabilities. By analyzing user queries and understanding search intent, the assistant can identify relevant products across online retailers. Price comparison features were implemented to ensure users get the best deals. Web scraping techniques were employed to gather product reviews, which were then analyzed using sentiment analysis to provide insights into user experiences. The final piece of the puzzle involved building a user-friendly interface to present this information in a clear and concise manner. Challenges Faced Data quality and consistency across different e-commerce platforms proved to be a hurdle. Extracting meaningful insights from often subjective product reviews required careful tuning of the sentiment analysis model. Balancing the comprehensiveness of the analysis with a user-friendly presentation was another key challenge. Looking Ahead The Gemini-Powered PSA represents an ongoing learning experience. Future iterations aim to incorporate features like wish list management and price alerts.