To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process.

Incorporating Search and Recommendation (S R) services within a singularapplication is prevalent in online platforms, leading to a new task termedopen-app motivation prediction, which aims to predict whether users initiatethe application with the specific intent of information searching, or toexplore recommended content for entertainment. Studies have shown thatpredicting users' motivation to open an app can help to improve user engagementand enhance performance in various downstream tasks. However, accuratelypredicting open-app motivation is not trivial, as it is influenced byuser-specific factors, search queries, clicked items, as well as their temporaloccurrences. Furthermore, these activities occur sequentially and exhibitintricate temporal dependencies. Inspired by the success of the Neural HawkesProcess (NHP) in modeling temporal dependencies in sequences, this paperproposes a novel neural Hawkes process model to capture the temporaldependencies between historical user browsing and querying actions. The model,referred to as Neural Hawkes Process-based Open-App Motivation prediction model(NHP-OAM), employs a hierarchical transformer and a novel intensity function toencode multiple factors, and open-app motivation prediction layer to integratetime and user-specific information for predicting users' open-app motivations.To demonstrate the superiority of our NHP-OAM model and construct a benchmarkfor the Open-App Motivation Prediction task, we not only extend the public S Rdataset ZhihuRec but also construct a new real-world Open-App MotivationDataset (OAMD). Experiments on these two datasets validate NHP-OAM'ssuperiority over baseline models. Further downstream application experimentsdemonstrate NHP-OAM's effectiveness in predicting users' Open-App Motivation,highlighting the immense application value of NHP-OAM.