In Situ Insights.

When consuming content in applications such as e-readers, word processors, and Web browsers, users often see mentions to topics (or concepts) that attract their attention. In a scenario of significant practical interest, topics are explored in situ, without leaving the context of the application: The user selects a mention of a topic (in the form of continuous text), and the system subsequently recommends references (e.g., Wikipedia concepts) that are relevant in the context of the application. In order to realize this experience, it is necessary to tackle challenges that include: users may select any continuous text, even potentially noisy text for which there is no corresponding reference in the knowledge base; references must be relevant to both the user selection and the text around it; and the real estate available on the application may be constrained, thus limiting the number of results that can be shown. In this paper, we study this novel recommendation task, that we call in situ insights: recommending reference concepts in response to a text selection and its context in-situ of a document consumption application. We first propose a selection-centric context language model and a selection-centric context semantic model to capture user interest. Based on these models, we then measure the quality of a reference concept across three aspects: selection clarity, context coherence, and concept relevance. By leveraging all these aspects, we put forward a machine learning approach to simultaneously decide if a selection is noisy, and filter out low-quality candidate references. In order to quantitatively evaluate our proposed techniques, we construct a test collection based on the simulation of the in situ insights scenario using crowdsourcing in the context of a real-word e-reader application. Our experimental evaluation demonstrates the effectiveness of the proposed techniques.