Mobile app retrieval for social media users via inference of implicit intent in social media text

People often implicitly or explicitly express their needs in social media in the form of "user status text". Such text can be very useful for service providers and product manufacturers to proactively provide relevant services or products that satisfy people's immediate needs. In this paper, we study how to infer a user's intent based on the user's "status text" and retrieve relevant mobile apps that may satisfy the user's needs. We address this problem by framing it as a new entity retrieval task where the query is a user's status text and the entities to be retrieved are mobile apps. We first propose a novel approach that generates a new representation for each query. Our key idea is to leverage social media to build parallel corpora that contain implicit intention text and the corresponding explicit intention text. Specifically, we model various user intentions in social media text using topic models, and we predict user intention in a query that contains implicit intention. Then, we retrieve relevant mobile apps with the predicted user intention. We evaluate the mobile app retrieval task using a new data set we create. Experiment results indicate that the proposed model is effective and outperforms the state-of-the-art retrieval models.