As Artificial Intelligence (AI) demonstrates an impressive performance across various tasks, its ability to process diverse data types has been widely adopted. However, AI models often lack transparency and interpretability, making it challenging to identify the most effective data categories and models for specific contexts. Current strategies typically rely on common sense or exhaustive searches, both of which are inefficient and costly. To fill this gap, we propose the INdicator-assisted FramewOrk (INFO). INFO enables researchers and practitioners to directly identify effective data categories and guide model selection for tasks involving multiple data categories. By assessing the inherent signal strength of the data, INFO provides a proxy for evaluating task performance, thereby reducing the need for time-consuming model trials and reproducibility efforts. We validate the effectiveness of INFO using real-world datasets across five different domains, focusing on the critical task of user churn detection as the case study. Our experiments show that INFO can reveal the efficacy of data categories before large-scale trials. INFO shows promise as an automated data engineering methodology that clarifies the relationship between model performance and dataset characteristics.

Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user’s booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (POIs) recommendation are mainly focused on users’ historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users’ historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of POIs nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users’ next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro F1-score.