Context-aware Product Recommendations Using Weather Data and AI Models

Abstract

Traditional recommender systems generate product suggestions based on user purchase history or collaborative filtering techniques. Although effective under static conditions, dynamic contextual factors, such as weather conditions and geographic location, are frequently overlooked despite their clear influence on consumer behavior. To overcome these limitations, context-aware recommender systems integrate real-time situational data, including ambient temperature, precipitation, and demographic attributes, into the recommendation process, therefore providing more precise and relevant suggestions.An author-developed framework employs OpenAI’s GPT-3.5 Turbo and GPT-4 variants to produce personalized order recommendations. Upon receiving a user-provided location (e.g., “Pula”), current weather data are retrieved from an external API and combined with user profile information to construct contextualized prompts. These prompts are sent directly to the OpenAI API, which returns context-aware recommendations based on the provided inputs. By merging environmental context and user preferences with advanced generative AI, alongside a given product database, recommendations are demonstrated to be substantially more relevant than those produced using traditional methods.

Keywords: CARS (context aware recommender systems), generativeAI, prompt engineering

DOI: 10.54941/ahfe1006786