Yahoo! Inc.

Anirban Dasgupta

Belle L Tseng

Shanmugasundaram Ravikumar

Liang Zhang

Lee Rhodes

Kevin John Lang

Kunal Punera

Achint Oommen Thomas

Maxim Gurevich

Arpita Ghosh

Andrei Z Broder

Vanja Josifovski

Vishwanath Tumkur Ramarao

Wei Chu

Kilian Quirin Weinberger

Yehuda Arie Koren

Tamas Sarlos

Xiaopeng Xi

Jyh-Shin Shue

Jay Pujara

Martin Zinkevich

Benjamin Graham

Arun A Prakash

Petros Drineas

Boulos Harb

Michael William Mahoney

Hung-Kuo (James) Chu

In accordance with one aspect, methods and apparatus facilitate the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers.

Apparatus and methods for classifying senders of unsolicited bulk emails

Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.

User trustworthiness

Techniques are provided for improving the speed and accuracy of analytics on big data using theta sketches, by converting fixed-size sketches to theta sketches, and by performing set operations on sketches. In a technique for performing a set operation, two sketches are analyzed to identify the maximum value of each sketch. The maximum values of the two sketches are compared. Based the comparison, one or more values are removed from the sketch whose maximum value is greater. After the removal, a set operation (e.g., union, intersection, or difference) is performed based on the modified sketch and the unmodified sketch. A result of the set operation is a third sketch, which may be used to estimate a cardinality of the larger data sets that are represented by the two input sketches.

System and method for performing set operations with defined sketch accuracy distribution

A method of generating a time managed challenge-response test is presented. The method identifies a geometric shape having a volume and generates an entry object of the time managed challenge-response test. The entry object is overlaid onto the geometric shape, such that the entry object is distributed over a surface of the geometric shape, and a portion of the entry object is hidden at any point in time. The geometric shape is rotated, which reveals the portion of the entry object that is hidden. A display region on a display is identified for rendering the geometric shape and the geometric shape is presented in the display region of the display.

Time managed challenge-response test

Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device.

Clustering cookies for identifying unique mobile devices

A system for resolving disputes in an online answers community is disclosed. The system improves the system's ability to resolve reports of abuse.

Jury system for use in online answers environment

Embodiments presented herein provide methods, systems and computer program products for determining a count of network users. One method identifies one or more login access requests, from one or more server logs. Each of the one or more login access requests comprises a login cookie, and a user identifier. The method then forms one or more connected components based on the one or more login access requests. Each connected component comprises one or more user identifiers, wherein each of the one or more user identifiers is associated with one or more login cookies. The method finally determines a count of login users based, at least in part, on the user identifiers in the one or more connected components.

System and method for counting network users

A system and method for implementing a multi-step challenge and response test includes steps or acts of: using an input/output subsystem for presenting a series of challenges to a user that require said user to correctly solve each challenge before a next challenge is revealed to the user; receiving the user's response to each challenge; and submitting a last response in the series of challenges to a server for validation. The method further includes: using a processor device configured to perform for each challenge in the series of challenges: internally validating the response by comparing the user's response to a correct response; and using the user's response, decrypting the next challenge to reveal the next challenge; wherein the next challenge remains obfuscated until a previous challenge is correctly solved.

Multi-step captcha with serial time-consuming decryption of puzzles

Method, system, and programs for computing similarity. Input data is first received from one or more data sources and then analyzed to obtain an input feature vector that characterizes the input data. An index is then generated based on the input feature vector and is used to archive the input data, where the value of the index is computed based on an improved Johnson-Lindenstrass transformation (FJLT) process. With the improved FJLT process, first, the sign of each feature in the input feature vector is randomly flipped to obtain a flipped vector. A Hadamard transformation is then applied to the flipped vector to obtain a transformed vector. An inner product between the transformed vector and a sparse vector is then computed to obtain a base vector, based on which the value of the index is determined.

Method and system for fast similarity computation in high dimensional space

An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features.

System and method of feature selection for text classification using subspace sampling

Embodiments of the present inversion relate to a two-pass compression scheme that achieves compression performance on par with existing methods while admitting individual message decompression. These methods provide both storage savings and lower end-user latency. They preserve the advantages of standard text compression in exploiting short-range similarities in data, while introducing a second step to take advantage of long-range similarities often present in certain types of structured data, e.g. email archival files.

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Berkeley, CA, United States of America

Anirban Dasgupta