Abstract: Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.

Abstract: An online system identifies an opportunity to present content to a viewing user and sends candidate content items eligible for presentation to the viewing user for display to a client device associated with the viewing user. The online system computes an estimated value associated with presenting a candidate content item to the viewing user based at least in part on a bid amount associated with the candidate content item and communicates the value to a content-providing user associated with the candidate content item. Based on information received from the client device describing the placement of one or more candidate content items within a feed of content items presented to the viewing user, the online system calculates a detrimental effect of presenting the candidate content item on user engagement with the online system by the viewing user and computes an adjusted value associated with its presentation based on the detrimental effect.

Abstract: Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.

Abstract: Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.