MAWI Rec: Leveraging Severe Weather Data in Recommendation
Brendan Andrew Duncan, Surya Teja Kallumadi, Taylor Berg-Kirkpatrick, Julian McAuley · 2024
Inferring user intent in recommender systems can help performance but is difficult because intent is personal and not directly observable. Previous work has leveraged signals to stand as a proxy for intent (e.g. user interactions with resource pages), but such signals are not always available. In this paper, we instead recognize that certain events, which are observable, directly influence user intent. For example, after a flood, home improvement customers are more likely to undertake a renovation project to dry out their basement. We introduce MAWI Rec, a recommender system that leverages severe weather data to improve recommendation. Our weather-aware system achieves a significant improvement over a state-of-the-art baseline for online and in-store datasets of home improvement customers. This gain is most significant for weather-related product categories such as roof panels and flashings.