No abstract

In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent "themes". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).

The largest publicly available knowledge repositories, such as Wikipedia and Freebase, owe their existence and growth to volunteer contributors around the globe. While the majority of contributions are correct, errors can still creep in, due to editors' carelessness, misunderstanding of the schema, malice, or even lack of accepted ground truth. If left undetected, inaccuracies often degrade the experience of users and the performance of applications that rely on these knowledge repositories. We present a new method, CQUAL, for automatically predicting the quality of contributions submitted to a knowledge base. Significantly expanding upon previous work, our method holistically exploits a variety of signals, including the user's domains of expertise as reflected in her prior contribution history, and the historical accuracy rates of different types of facts. In a large-scale human evaluation, our method exhibits precision of 91% at 80% recall. Our model verifies whether a contribution is correct immediately after it is submitted, significantly alleviating the need for post-submission human reviewing.