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A critical role of online platforms like Facebook, Wikipedia, YouTube, Amazon, Doordash, and Tinder is to moderate content. Interventions like banning users or deleting comments are carried out thousands of times daily and can potentially improve our onlin ...
Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...
Computing the count of distinct elements in large data sets is a common task but naive approaches are memory-expensive. The HyperLogLog (HLL) algorithm (Flajolet et al., 2007) estimates a data set's cardinality while using significantly less memory than a ...
Due to the rising demand for large-scale data processing, there is a growing interest in both batch processing, where large volumes of data are processed offline, and stream processing, where large quantities of streaming data are processed online. The dic ...
In this paper we construct a “reflexivity” index for Bitcoin cryptocurrency that measures the amount of activity generated endogenously within the market. For this purpose we fit a univariate self-exciting Hawkes process with two-classes of parametric kern ...
Social buttons are now widespread in social media apps. They are used to assign weight to user content and trigger user engagement. They come in different shapes (e.g., thumb in Facebook, arrows in Reddit or StackOverflow, plus one in Google+) but very lit ...
A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the ...
