Will you go where you search? A deep learning framework for estimating user search-and-go behavior.

[Display omitted] • To the best of our knowledge, our work is the first attempt to model search-and-go human behavior as a CTR-like binary classification problem in recommender system. • We propose an effective end-to-end deep learning model with a wide&deep architecture by utilizing LSTM attention and factorization-machine, to learn the sophisticated interactions between mobility data and search data. • We evaluate the framework on real-world dataset collected from Yahoo! Japan portal application, and demonstrate satisfactory performances on multiple search-and-go scenarios at a nation-wide level. • Our propsed model DeepSTIN achieved better performances comparing with the baseline models such as GBDT, DNN, Factorization-supported Neural Network [48], Product-based Neural Network [26], Wide&Deep [2], Deep & Cross Network [31], DeepFM [10], and XDeepFM [19]. • DeepSTIN improves the performance of DeepFM on four datasets by {6.4%, 7.9%, 11.8%, 9.3%} in AUC and {7.8%, 8.5%, 22.0%, 26.8%} in LogLoss. Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such behavior can be of great importance for Internet companies to recommend point-of-interest (POI), advertisement, and route, as well as for governments and public service operators like metro companies to conduct traffic monitoring, crowd management, and transportation scheduling. Therefore, in this study, we first collect search log data and GPS log data with linked and consistent user ID from Yahoo! Japan portal application installed in millions of smart-phones and tablets. Then we propose a framework including a complete data-processing procedure and an end-to-end deep learning model to predict whether a user will check-in the searched place or not. Specifically, as users' daily activities are considered to have high correlation with their travel, eating, and recreation decision in the future (i.e., go or not), Deep Spatial–Temporal Interaction Network (DeepSTIN) is elaborately designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data. Experimental results based on the standard metrics demonstrate that our proposed framework can achieve satisfactory performances on multiple real-world search scenarios. [ABSTRACT FROM AUTHOR]