Query-Less: Predicting Task Repetition for NextGen Proactive Search and Recommendation Engines

Web search has been a reactive scenario for decades which often starts by users issuing queries. By studying the user behavior in search engine logs, we have discovered that many of the search tasks such as stock-price checking, news reading exhibit strong repeated patterns from day to day. In addition, users exhibit even stronger repetition on mobile devices. This provides us chances to perform proactive recommendations without user issuing queries. In this work, we aim at discovering and characterizing these types of tasks so that we can automatically predict when and what types of tasks will be repeated by the users in the future, through analyzing search logs from a commercial Web search engine and user interaction logs from a mobile App that offers proactive recommendations. We first introduce a set of novel features that can accurately capture task repetition. We then propose a novel deep learning framework that learns user preferences and makes automatic predictions. Our framework is capable of learning both user-independent global models as well as catering personalized models via model adaptation. The model we developed significantly outperforms other state-of-the-art predictive models by large margins. We also demonstrate the power of our model and features through an application to improve the recommendation quality of the mobile App. Results indicate a significant relevance improvement over the current production system.