Modeling weather context dependent food choice process

Abstract

In this paper, we investigate the impact of weather context on the process of choosing foods. We mine social media on the web to create datasets on food choice, and associate food selections with weather context gathered from governmental meteorological information. From the dataset, we find that not only weather but also food events or social events on special days significantly impacts food choice. Accordingly, we propose a topic model that include the event class to represent the relationship between weather context and food choice. We quantitatively evaluated the model by perplexity, and discovered that considering both weather and event context improves prediction performance. Perplexity of the proposed model (weather and event context-aware topic model with separate topics) is (4663.0), which beats the benchmark model (4943.4). An analysis shows that combining contexts in the topic generation process yields better results that combining contexts in the word generation process. We also conduct a qualitative evaluation on the learned topic and associated foods.